1. Executive Summary

The Department of Defense is currently undergoing a structural transformation in its approach to force projection, characterized most prominently by rapid acquisition strategies such as https://www.defense.gov/. By aiming to field attritable, autonomous systems at the scale of multiple thousands across multiple domains, the military is transitioning from a reliance on small numbers of exquisite, highly survivable platforms to a posture that leverages mass, autonomy, and expendability.¹This strategic pivot is designed to impose operational dilemmas on pacing threats, providing commanders with thousands of sensing and striking nodes that can be deployed with a high tolerance for battlefield loss.⁴However, the acceleration of system acquisition and forward deployment has vastly outpaced the logistical, environmental, and doctrinal frameworks required to manage the end-of-life phases of these very systems.

While the defense industrial base focuses intensely on mass production techniques modeled after the commercial automotive sector ⁴, a critical oversight remains unaddressed by policymakers and tacticians alike: the systemic demilitarization, data sanitization, and hazardous waste disposal of massed drone fleets. The concept of attritable systems, by definition, implies that thousands of units will be lost in combat, degraded by environmental wear, or rendered obsolete at unprecedented rates. The current Department of Defense disposal architecture is engineered for low-volume, high-value assets. Attempting to force thousands of toxic, degraded, and highly classified unmanned aerial systems through legacy reverse-logistics pipelines will inevitably create critical bottlenecks, severe in-theater safety hazards, and profound operational security vulnerabilities.⁶

This report provides a strategic analysis of the unaddressed tail-end of the unmanned aerial system lifecycle. It focuses on the dual imperatives of the disposal process: physical hazard mitigation and intelligence protection. First, the report examines the massive logistical burden and environmental danger posed by lithium-ion battery stockpiles, which present severe thermal runaway and toxic gas hazards in forward operating environments.⁸ Second, it addresses the critical requirement for automated data sanitization and physical anti-tamper mechanisms to prevent adversarial reverse-engineering of downed systems—a threat historically validated by the capture of advanced platforms in hostile territory.¹⁰

To sustain the operational advantages of massed drone fleets without generating crippling logistical liabilities or intelligence hemorrhages, Department leadership must elevate the demilitarization and disposal lifecycle to the same priority level as initial acquisition. This requires establishing standardized protocols for field-expedient battery inerting, mandating cryptographically secure zeroization architectures within flight controllers, scaling the Defense Logistics Agency’s expeditionary disposal capabilities, and integrating sustainable remediation practices into all theater planning.¹²

2. The Operational Realities of Massed Attritable Systems

The strategic logic underpinning the procurement of massed unmanned systems is unassailable in the context of modern great-power competition. Legacy drone platforms, such as the RQ-4 Global Hawk or the MQ-9 Reaper, require extensive logistical footprints, large maintenance crews, and specialized airport infrastructure.¹⁶ They represent exquisite capabilities that cannot be easily replaced if lost to enemy air defenses. In contrast, the current trajectory favors systems that are small, smart, cheap, and numerous.³ This philosophy seeks to overwhelm adversary targeting systems, forcing them to expend expensive kinetic interceptors on inexpensive platforms, thereby creating a favorable cost-exchange ratio.

The scale of the disposal challenge, however, scales linearly with the volume of deployment. The mandate to field systems in the thousands within tight eighteen-to-twenty-four-month operational windows forces a fundamental reevaluation of what happens when these systems fail, degrade, or are superseded by iterative software and hardware upgrades.¹ Unlike traditional aircraft, which undergo decades of sustainment, depot-level maintenance, and carefully managed lifecycles, attritable drones will experience rapid, almost disposable lifecycles. A fleet of thousands of tactical drones with an average operational lifespan of twelve to eighteen months will result in hundreds of units entering the disposal pipeline every single month.

The term “attritable” creates a dangerous semantic hazard within logistics planning. It implies that these systems can simply be abandoned on the battlefield, written off the property books, or discarded in standard waste streams once they fulfill their mission. This is a profound operational fallacy. Even the most inexpensive tactical drone contains specific elements that strictly prohibit casual abandonment. They utilize high-energy density power sources, specifically lithium-ion or lithium-polymer batteries, that pose acute fire, explosion, and chemical hazards if damaged or improperly stored.⁹ They possess sensitive digital storage media, including flight controllers, telemetry logs, and optical payloads, that contain precise operational data, base locations, command frequencies, and network authentication keys.¹³ Furthermore, they are assembled using controlled hardware components, such as specialized sensors, anti-jam antennas, and encryption modules, that require formal trade security controls and worldwide mutilation under specific Controlled Inventory Item Codes.⁶

When operating in contested logistical environments, the assumption that frontline units can seamlessly retrograde these hazardous and classified materials back to safe havens or continental United States processing facilities is deeply flawed. The modern battlefield features contested supply lines, anti-access/area denial networks, and constant surveillance, meaning forward-deployed units must manage their own waste and wreckage under severe duress.²¹ Therefore, the disposal architecture must be pushed as far forward to the tactical edge as possible, requiring entirely new paradigms for field-expedient demilitarization.

3. Regulatory Frameworks Governing Demilitarization and Disposal

To understand the systemic risk posed by the rapid influx of unmanned systems, it is necessary to examine the regulatory architecture that governs military property disposal. The overarching guidance is provided by the(https://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodm/416028m_vol1.pdf), which stipulates that demilitarization is an inherent life-cycle requirement, not an afterthought confined merely to the end of a system’s utility.⁶ The Defense Acquisition System requires that Department of Defense Components generate programmatic demilitarization plans prior to developmental test and evaluation, and certainly before releasing any new system or item to a non-military activity.⁶

These Demilitarization Plans are bifurcated into two distinct categories. Programmatic Demilitarization Plans are tailored to each acquisition program and addressed early in the process, outlining what tasks need to be performed and formulating the overarching strategies for disposition processing. Procedural Demilitarization Plans provide the actual, granular “how-to” instructions for performing physical demilitarization, developed using existing technical data, operating manuals, and technical drawings.⁶ The Department utilizes specific demilitarization codes to identify requirements for processing excess materiel, indicating whether items require physical destruction, mutilation, or trade security control measures.⁶

However, the speed of modern commercial-off-the-shelf procurement and rapid fielding initiatives often marginalizes this rigid requirement. When rapid acquisition strategies push prototypes and commercially derived drones directly to end-users to meet urgent operational needs, the corresponding procedural plans are frequently delayed, under-developed, or entirely absent. This creates a scenario where frontline troops are issued advanced hardware without clear instructions or the necessary equipment to safely and legally dispose of it when it breaks or becomes obsolete.

Furthermore,(https://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodm/416021_vol1.pdf) governs the disposal of personal property, including the stringent requirements for managing hazardous waste and materials requiring special handling.²² This manual mandates that hazardous waste disposal comply with the Resource Conservation and Recovery Act, managed through a worldwide network of hazardous waste management contracts.²⁴ The intersection of these two regulatory bodies—one demanding the physical destruction of sensitive components and the other demanding the careful containment of hazardous chemical waste—creates a complex operational dilemma when dealing with an integrated unit like a drone, where the classified circuit board is inextricably linked to the volatile lithium battery.

4. Intelligence Exploitation Vectors and Mitigation Strategies

The most immediate strategic risk associated with massed drone operations is the unintentional transfer of technology, cryptographic material, and operational intelligence to pacing threats. By saturating an airspace with thousands of sensors and communication nodes, the military statistically guarantees that a certain percentage of these systems will experience mechanical failure, electronic warfare disruption, or kinetic interception, resulting in relatively intact airframes falling into hostile territory.²⁶

Adversaries possess highly organized, state-sponsored programs dedicated entirely to the recovery and exploitation of Western military technology. The loss of a United States RQ-170 Sentinel drone in Iranian territory in December 2011 serves as the foundational case study for this specific vulnerability.¹⁰ The aircraft, which landed largely intact due to alleged electronic spoofing, was subjected to intense scrutiny by Iranian aerospace engineers. Despite initial assumptions by American officials that the internal software was heavily encrypted and structurally secure from intrusion, the capture allowed adversarial engineers to decode flight data, reverse-engineer the physical aerodynamic design, and eventually mass-produce indigenous replicas of the stealth platform.¹¹ Similarly, the capture and exploitation of smaller, less exquisite systems, such as the ScanEagle, provided adversaries with advanced aerodynamic and sensor insights that allowed them to bypass decades of organic research and development.²⁷

More recently, the ongoing conflict in Ukraine has demonstrated the speed at which tactical drone wreckage is exploited on the modern battlefield. Recovered printed circuit boards, telemetry modules, and optical sensors are immediately analyzed by specialized cyber units, such as the Russian military intelligence-connected group Sandworm, to identify supply chains, uncover frequency hopping algorithms, and develop counter-electronic warfare profiles.²⁹ If thousands of American attritable drones are deployed without absolute data destruction fail-safes, they will serve as an involuntary technology transfer program, providing adversaries with the exact specifications needed to defeat American swarms.

The intelligence vectors present in a downed drone are multifaceted. Telemetry and flight logs map friendly base locations, patrol routes, and operational tempo. Optical and sensor payloads reveal collection capabilities, resolution limits, and targeting algorithms. Command and control transceivers expose frequency hopping schemes and allow adversaries to develop targeted jamming or spoofing profiles. Finally, the airframe aerodynamics and materials provide blueprints for reverse-engineering lift, stealth, and propulsion metrics for indigenous production. Each of these vectors requires a dedicated, distinct approach to sanitization and destruction to assure operational security.

5. Doctrinal Data Sanitization: Bridging the Gap to the Tactical Edge

The National Security Agency and Central Security Service maintain rigorous standards governing the sanitization and destruction of information system storage devices, detailed comprehensively in Policy Manual 9-12.³¹ The manual defines sanitization as the removal of information from a storage device such that data recovery using any known technique or analysis is definitively prevented.³³ Approved methods for achieving this standard include degaussing, high-temperature incineration, mechanical shredding, and disintegration.³³

However, translating these facility-based, industrial requirements to a lightweight tactical drone operating beyond the forward line of own troops presents severe engineering and operational challenges. National Security Agency guidelines explicitly note that rudimentary techniques such as bending, cutting, or using field-expedient emergency procedures—such as firing a weapon into a storage device—may leave portions of the media undamaged and fully accessible using advanced laboratory forensics.¹³ Therefore, kinetic destruction via bullet or crash impact is wholly insufficient for sanitizing highly classified cryptographic keys, mission profiles, or collected intelligence logs.

To effectively manage this risk without burdening the operator, Department of Defense leadership must require automated, zero-trust architectures integrated directly into the flight controllers and hardware of attritable fleets.³⁵

Cryptographic Erase and Logical Sanitization

Software-based wiping methods, such as the legacy DoD 5220.22-M standard involving multiple overwrite passes, are obsolete and no longer approved for highly sensitive data by modern intelligence agencies.³⁶ Furthermore, they require time that a plummeting drone does not possess. Modern attritable systems must instead utilize Cryptographic Erase functionality. This mechanism involves the instantaneous destruction of the encryption key that protects the data on the device, rendering the remaining cipher text permanently unreadable regardless of physical recovery.¹³ This logical sanitization must be designed to trigger automatically upon detecting specific conditions: unauthorized hardware access, sustained loss of connection with the ground station, or the initiation of a forced landing or crash sequence.³⁷

Anti-Tamper Hardware and Physically Unclonable Functions

To prevent sophisticated adversaries from cloning microchips or bypassing software-based wipes, defense contractors must integrate anti-tamper packaging and Physically Unclonable Functions into the drone’s architecture.³⁸ Physically Unclonable Functions leverage microscopic, atomic-level manufacturing variations inherent in silicon wafers to generate private encryption keys on demand, rather than storing them statically within the drone’s memory. If the physical structure of the chip is altered, probed, or subjected to electron microscopy by an adversary attempting to extract data, the unique physical characteristics change irreversibly, and the key can no longer be generated.³⁸ This provides a robust, hardware-level defense against reverse engineering.

Emergency Destruct Mechanisms

For highly sensitive intelligence payloads where logical sanitization is deemed insufficient, it must be paired with guaranteed physical destruction. Autonomous self-destruct circuits utilizing small thermite charges or high-power micro-incinerators can ensure that the internal electronics are subjected to temperatures exceeding the National Security Agency requirement of 670 degrees Celsius for magnetic drives, or 233 degrees Celsius for solid-state and composite equivalents.³¹ While the inclusion of incendiary devices inherently complicates the peacetime transportation, storage, and handling of the drones, it is an unavoidable necessity for operating classified sensors in highly contested airspace where recovery is impossible.³⁹

The Forensics of Friendly Recovery

When drones are recovered by friendly or allied forces, the chain of custody must be impeccably maintained to preserve forensic data and prevent accidental triggering of security protocols. Law enforcement, explosive ordnance disposal, and intelligence units frequently recover downed systems, both friendly and hostile.²⁰ Recovered drones must be immediately shielded using Radio Frequency isolation techniques, such as portable Faraday enclosures or specialized transport sacks, to prevent remote detonation, data exfiltration, or adversarial triggering of zeroize mechanisms during transport to exploitation laboratories.²⁰ Standardizing these recovery protocols across international partners is governed by agreements such as NATO STANAG 3531, which dictates combined investigation parameters and wreckage recovery procedures.⁴⁰ Ensuring all allied partners understand how to handle these systems without compromising the intelligence or triggering the emergency destruct mechanisms is a critical component of coalition interoperability.

6. The Kinetic and Chemical Hazards of Lithium-Ion Power Sources

While data exploitation poses a severe non-kinetic threat to operational security, the physical batteries powering these drone fleets present an immediate, lethal, and compounding kinetic hazard to logistics personnel and combat troops. Massed drones rely almost exclusively on lithium-ion and lithium-polymer batteries due to their exceptional energy density, low self-discharge rate, and overall operational performance.⁴³ However, as the Department of Defense transitions to scaled drone procurement, the logistics system must absorb millions of pounds of highly volatile chemical energy storage.

The Mechanics of Thermal Runaway

Lithium batteries are inherently unstable when subjected to mechanical damage such as crushing or puncturing, electrical abuse such as overcharging or short circuits, or extreme ambient temperatures—all of which are exceedingly common occurrences in rugged tactical environments.¹⁹ The primary danger is thermal runaway, an uncontrollable, self-heating state initiated when internal cell temperatures reach a critical threshold, often due to an internal short circuit.⁸

During a thermal runaway event, the internal chemical reactions generate tremendous heat, often rapidly exceeding 1,000 degrees Fahrenheit, which in turn accelerates the reaction in adjacent cells, creating a highly destructive positive feedback loop.⁸ The resulting fires are notoriously difficult for military firefighters and damage control personnel to extinguish. Standard halon suppression systems and conventional fire retardants only extinguish the open flame; they do not halt the internal chemical reaction, which creates its own fuel and oxygen byproducts as the electrolyte breaks down.¹⁹ Consequently, lithium batteries frequently reignite hours or even days after the initial fire appears to be fully extinguished, vastly complicating post-incident transport, cleanup, and disposal.⁴⁵

Toxic Gas Emissions and Battlefield Health Risks

The visible flames and extreme heat are only a secondary hazard. The primary danger to personnel operating in forward operating bases, vehicle convoys, or enclosed spaces such as ship decks or storage bunkers is the catastrophic release of toxic gases. During a failure event, the battery casing ruptures and vents a complex, highly pressurized mixture of volatile organic compounds, particulate matter, heavy metals, and lethal gases into the immediate environment.⁴⁴

The most concerning emission generated during lithium-ion thermal runaway is Hydrogen Fluoride.⁹ Hydrogen Fluoride is highly corrosive and extremely toxic. When inhaled by personnel in the vicinity, it reacts violently with the natural moisture in the respiratory tract and lungs to form hydrofluoric acid, causing deep tissue damage, severe pulmonary edema, and often fatal respiratory failure.⁹ Furthermore, massive volumes of Carbon Monoxide are released alongside the Hydrogen Fluoride. In close proximity to a thermal runaway event, Hydrogen Fluoride concentrations can rapidly reach hundreds of parts per million, vastly exceeding all permissible occupational exposure limits and creating an immediately deadly atmosphere for logisticians and first responders who may not be equipped with self-contained breathing apparatuses.⁴⁸

7. In-Theater Battery Management and Neutralization Technologies

When a tactical drone fleet reaches the end of its operational life, or when batteries naturally degrade through standard charge and discharge cycles, units are left holding thousands of volatile hazardous waste items. Under the Resource Conservation and Recovery Act administered by the Environmental Protection Agency, these specific types of batteries are classified as hazardous waste and require highly regulated handling procedures, specialized protective packaging, and specific, documented disposal pathways.²⁵

Currently, the physical transport of these end-of-life batteries out of a combat theater is prohibitively expensive and logistically dangerous. Transporting unstable, degraded lithium batteries on military cargo aircraft or naval vessels introduces unacceptable, catastrophic risks to the transport platform itself.¹⁹ The Department of the Navy’s Lithium Battery Safety Program strictly regulates these transport mechanisms, emphasizing the grave danger of latent defects causing mid-flight thermal events that could result in the loss of major fleet assets.⁴³

To decrease the financial cost and mitigate the physical risk to the Department of Defense, the Defense Logistics Agency Research and Development team, operating through specialized programs like the Battery Network, is actively collaborating with industry partners to develop cutting-edge technologies designed to render lithium batteries inert directly in the field.¹²

The strategic goal of these initiatives is to develop reliable chemical or mechanical processes that can safely discharge and permanently neutralize the reactive internal elements of the battery at the forward operating base, without requiring transport to a specialized facility. If a battery can be reliably inerted, it removes the immediate, localized threat of thermal runaway, officially reclassifies the component from a hazardous explosive risk to standard solid waste, and drastically reduces the financial and logistical burden of retrograding the material back to the continental United States for final processing.²³ Until this specific inerting technology is fully matured, manufactured, and distributed to frontline units, commanders will be forced to stockpile dangerous, highly reactive waste in active war zones. This creates soft, high-value targets for adversarial kinetic strikes or sabotage, which could easily trigger massive secondary explosions and toxic gas clouds within friendly perimeters.

8. Environmental Compliance, Remediation, and the DERP Parallel

The intersection of massed drone disposal and environmental compliance represents a severe regulatory and geopolitical challenge that extends far beyond the immediate battlefield. The extraction and processing of materials inherent to drone manufacturing—such as lithium, cobalt, and titanium—already cause significant global ecological degradation.⁵⁴ Discarding thousands of drones in theater not only wastes these critical, increasingly scarce resources and heightens dependence on foreign supply chains, but it also creates lasting environmental contamination that will inevitably require remediation.⁵⁴

In extreme combat environments where retrograde logistics are contested or impossible, units may be forced to dispose of drones and batteries on-site. Military doctrine permits the burial or burning of certain wastes, provided it strictly aligns with Host Nation environmental laws and established theater standard operating procedures.⁵⁷ However, these traditional waste management methods are heavily restricted when applied to modern electronic components.

The incineration of hardware containing hazardous materials, heavy metals, and reactive lithium batteries is strictly prohibited due to the acute risk of explosions and the lofting of highly toxic dioxins and corrosive gases into the atmosphere.⁴⁷ Open-air burn pits, which have caused massive, well-documented historical health crises for United States veterans, absolutely cannot be utilized to dispose of attritable unmanned aerial system fleets.

Burial presents similar, though less immediate, long-term risks. Government-approved landfills must feature secure perimeter fencing, restricted access, and formally witnessed burial procedures.²³ When lithium batteries are buried without the use of a complete discharge device, they remain chemically reactive and can leach heavy metals and toxic compounds into the host nation’s groundwater. This leads to long-term ecological damage and severe diplomatic friction with allied partners who must deal with the contamination long after combat operations have ceased.²³

The Department of Defense must view the disposal of massed drone fleets through the historical lens of the Defense Environmental Restoration Program.¹⁴ Currently, the Department is expending billions of dollars and immense political capital to remediate sites contaminated by per- and polyfluoroalkyl substances found in legacy firefighting foams.⁶⁰ If the disposal of lithium batteries and toxic drone components is not managed proactively and systemically today, the Department risks creating thousands of new micro-contamination sites across allied host nations. This will lead to future financial liabilities and remediation requirements that dwarf the initial, seemingly low acquisition costs of the drones themselves. Green and sustainable remediation practices must be integrated into the Replicator program’s lifecycle planning from inception, utilizing advanced modeling tools to optimize waste allocation, balance recycling capabilities, and minimize final disposal footprints.¹⁴

9. Forward-Deployed Reverse Logistics and Expeditionary Operations

To manage the overwhelming influx of end-of-life systems and hazardous materials, the Department of Defense relies heavily on the capabilities of Defense Logistics Agency Disposition Services.⁶² The Defense Logistics Agency manages the highly complex worldwide network responsible for the reutilization, transfer, demilitarization, and hazardous waste disposal of military property.²⁴

Recognizing that modern conflicts occur in austere, heavily contested environments, Defense Logistics Agency Distribution Expeditionary teams are specifically designed to deploy rapidly—often within a twenty-four to forty-eight-hour window—to establish scalable, end-to-end distribution and disposal processes directly in the theater of operations.¹⁵ These highly trained, multidisciplinary teams utilize portable Expeditionary Site Sets to provide combatant commands with immediate, robust disposal operations that comply with all regulatory frameworks.⁶⁵

However, the sheer volume of property handled by Disposition Services requires complex, commodity-based sorting procedures and heavily relies on automated electronic data transfer systems to maintain strict accountability and legal compliance.²⁴ When tasked with handling thousands of serialized drone components and simultaneously managing stockpiles of hazardous lithium batteries, the administrative and physical burden alone can overwhelm expeditionary capabilities and crash tactical supply networks. The system must be streamlined to handle mass rather than bespoke items.

A critical, yet historically underutilized, aspect of the Defense Logistics Agency’s mission is reutilization. Historically, only a small fraction of the property turned into the agency is successfully reutilized by other Military Services.⁷ For massed drone fleets, this paradigm must undergo a radical shift toward a circular economy model. Drones that are damaged in combat or grounded due to structural failure often contain fully functional, highly expensive sub-components, such as optical gimbals, secure transponders, encrypted communication modules, or specialized motors.

Disposition Services must establish rapid triage and harvesting protocols in-theater. Instead of grinding an entire damaged drone into scrap or burying it, expeditionary teams should be equipped and trained to extract high-value, high-scarcity components—particularly those utilizing rare-earth magnets and aerospace-grade materials—and immediately route them back into the active supply chain.⁵⁴ This approach directly supports the warfighter by mitigating acute supply chain disruptions, reducing the financial cost of replacement parts, and addressing the inherent vulnerability of relying on critical minerals sourced from geopolitically unstable regions.⁷

Furthermore, the proliferation of drones dictates that adversarial systems will also saturate the airspace, requiring robust counter-drone strategies and the subsequent management of hostile wreckage.²⁶ Technologies ranging from directed energy microwave weapons to cyber-takeover tools are employed to neutralize these threats.⁶⁶ When these hostile systems are brought down, they present the exact same toxic battery hazards and unique intelligence-gathering opportunities as friendly drones. Expeditionary teams and allied explosive ordnance disposal units must be equally prepared to process vast quantities of hostile wreckage, safely extracting digital forensics for intelligence analysis while meticulously managing the physical and chemical hazards.²⁰

10. Strategic Directives for Department Leadership

The Department of Defense cannot achieve sustainable lethality through mass without mastering the logistics of disposal. The rapid procurement of thousands of attritable systems solves the immediate tactical problem of magazine depth, but it creates a massive, trailing vulnerability in the form of hazardous waste and intelligence exposure. To close the critical vulnerabilities exposed by the rapid acquisition of these fleets, Department leadership must establish and enforce the following strategic disposal protocols:

1. Mandate Integrated Demilitarization Engineering in Acquisition The Defense Innovation Unit and all primary acquisition authorities must require vendors to include comprehensive, automated demilitarization capabilities as a core, non-negotiable performance metric. Drones procured under the Replicator initiative must possess hardware-level anti-tamper mechanisms and automated Cryptographic Erase functions that activate upon connection loss or catastrophic impact.¹³ Systems lacking these capabilities should be disqualified from procurement, as they represent unacceptable intelligence risks that negate their tactical value.

2. Accelerate and Fund Field-Expedient Battery Neutralization The Defense Logistics Agency Research and Development Battery Network program must receive prioritized, expedited funding to rapidly field battery-inerting technology.⁵¹ The ability to chemically or mechanically neutralize lithium-ion batteries at the tactical edge is the single most effective way to eliminate thermal runaway hazards, reduce toxic gas exposure to personnel, and bypass the crippling logistical costs of shipping reactive hazardous waste out of theater.⁹ This technology must become standard issue at all forward operating bases.

3. Expand Expeditionary Disposal Capabilities Defense Logistics Agency Distribution Expeditionary teams must be scaled, resourced, and specifically trained to handle the unique, high-volume demands of autonomous system disposal.¹⁵ This includes equipping Expeditionary Site Sets with industrial-grade media disintegrators capable of meeting National Security Agency standards for classified storage destruction in the field ¹³, as well as providing portable hazardous waste processing units designed specifically for lithium and heavy metal containment.

4. Establish a Circular “Harvesting” Doctrine Update disposal manuals to explicitly prioritize component harvesting over wholesale destruction for damaged drones. Establish forward-deployed triage centers where functional, high-value components can be quickly extracted, digitally sanitized of specific mission data, and reinserted into the supply chain to maintain operational readiness and reduce reliance on fragile commercial supply chains.⁷

5. Prohibit Unregulated In-Theater Disposal Strictly enforce prohibitions against the open-pit burning or unregulated burial of drones and lithium batteries.²³ Combatant Commanders must be provided with the logistical support necessary to manage these materials properly to prevent the creation of highly toxic environmental hazard sites that will inevitably incur billions in future remediation costs and severely damage host-nation relations.¹⁴

By proactively addressing the entirety of the end-of-life lifecycle of massed unmanned systems, the Department of Defense can ensure that the logistical and environmental burdens of these advanced technologies do not offset their intended tactical advantages. True operational mass is only achieved when the entire spectrum of the capability—from the commercial assembly line to ultimate, secure demilitarization—is comprehensively managed.

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1. Executive Summary

The United States Department of Defense (DoD) is actively pursuing substantial investments in artificial intelligence (AI) and autonomous drone technologies. The strategic objective is to field combat-credible, decentralized, and intelligent systems capable of operating at machine speed across contested multi-domain environments. However, while modernization efforts heavily prioritize platform capabilities—such as hardware procurement, airframe manufacturing, and sensor integration—there is a critical misalignment regarding the systemic requirements necessary to design, build, test, operate, and evolve these tools.¹ A pervasive tendency exists within the defense establishment to fixate on the physical technology itself while overlooking the underlying distributed systems infrastructure and evaluation mechanisms that make the technology viable.¹

This report provides a strategic evaluation of the critical bottlenecks within the Department’s Test, Evaluation, Verification, and Validation (TEVV) enterprise. The central finding indicates that legacy testing infrastructure, which was built for deterministic hardware and human-piloted systems, is fundamentally ill-equipped to handle non-deterministic AI behaviors, continuously learning algorithms, and distributed swarm logic.¹ Traditional physical test ranges are constrained by safety limitations, geographical boundaries, and an inherent inability to replicate the millions of edge-case scenarios required to validate reinforcement learning models.³ Furthermore, traditional regulatory mechanisms, such as static Safety Review Boards (SRBs) and point-in-time Authorization to Operate (ATO) certifications, create bureaucratic friction that stifles the rapid deployment and iterative updating of software-defined weapons.⁵

To bridge the gap between technological ambition and operational readiness, DoD leadership must pivot toward modernizing the TEVV ecosystem. This requires a systemic shift away from platform-centric acquisition toward architecture-centric and software-first paradigms. Strategic imperatives include the wide-scale adoption of highly realistic simulation environments and digital twins to enable hardware-in-the-loop (HITL) and software-in-the-loop (SITL) testing at scale.³ Regulatory frameworks must also evolve concurrently; leadership must champion the transition from static security reviews to Continuous Authority to Operate (cATO) protocols ⁹, and replace legacy Technology Readiness Levels (TRLs) with a nuanced AI Readiness Framework (AIRL) that accounts for data integrity, algorithmic alignment, and human-machine teaming.¹¹ Only by addressing these TEVV bottlenecks can the DoD ensure that warfighters are equipped with autonomous tools that are not only lethal and survivable but, fundamentally, trustworthy.

2. The Evolving Threat Landscape and the Autonomy Imperative

To understand the inadequacy of the current TEVV enterprise, it is necessary to examine how AI and autonomy alter the fundamental nature of military systems. The DoD’s integration of AI spans a broad spectrum, ranging from decision-support systems (AI-DSS) designed to accelerate the joint targeting cycle, to highly autonomous unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) capable of executing independent kinetic action when communications are severed.¹²

The Distinction Between Automation and Autonomy

A frequent point of confusion in defense acquisitions is the conflation of “automation” and “autonomy.” Automation refers to a system’s ability to undertake a narrow, constrained task with low levels of complexity, where that task is highly repetitive and independent of choice.¹⁵ Legacy flight control software, radar tracking algorithms, and autopilot waypoint navigation operate on pre-programmed, rules-based logic.⁵ Conversely, autonomy involves empowering a system to make “how” decisions to achieve a task within the constraints of defined parameters, requiring an inherent level of artificial intelligence to process variables and adapt to changing environments.¹⁶

The Shift from Deterministic to Non-Deterministic Systems

Historically, military aviation and maritime acquisitions have relied on deterministic systems. In a deterministic software framework, a specific input will always yield the exact same output. Testing these systems involves verifying that the software code correctly executes its programmed logic under defined parameters, usually through rigorous code-level hazard analysis.⁵

Modern military AI, particularly deep neural networks and reinforcement learning agents, is inherently non-deterministic. These models do not follow explicit, human-coded rules; instead, they recognize complex patterns within vast datasets and generate probabilistic outputs.¹² An autonomous drone trained via reinforcement learning may react differently to the same tactical scenario depending on subtle environmental variations, sensor noise, or adversarial electronic warfare (EW) interference. Consequently, traditional software testing methodologies—which rely on verifying every possible line of code or structural logic path—cannot be successfully applied to AI.⁵

The Historical Context of Software Failures in Combat

The stakes of failing to properly evaluate software-driven military systems are historically severe. Existing studies examining military accidents frequently utilize “normal accidents” and “high reliability organizations” theories, which highlight that software development life cycles often expand the causal timeline of accidents beyond immediate battlefield decisions to structural choices made years earlier in software design.¹⁸

During the Cold War, computerized early warning systems produced significant near-miss nuclear crises due to algorithmic misinterpretations. More recently, software integration flaws contributed to the 1988 USS Vincennes shootdown of an Iranian airliner, the 2003 Patriot missile fratricides, and the 2017 USS McCain collision.¹⁸ In the case of the USS McCain, naval reviews indicated that designers added automation without adequately considering the effects on operators trained on legacy equipment.¹⁸ As the DoD transitions to AI, the risk of devastating military accidents increases exponentially if the underlying software is decoupled from rigorous, operationally representative testing environments. AI applications deployed with subtle failure modes, warped incentives, or susceptibility to automation bias present an unacceptable operational risk.¹⁹

3. The Fundamentals of Machine Speed Warfare and True Swarm Architectures

A critical issue impeding TEVV modernization is the conceptual dilution of “swarming” within defense acquisitions. Current modernization discourse and industry marketing often conflate robotic maneuver en masse with true swarm intelligence. As highlighted by defense distributed systems experts, the deployment of dozens or even hundreds of drones controlled by a single operator, or following a pre-scripted leader-follower formation, does not constitute a true swarm.¹

The Illusion of Plurality vs. Singular Cohesion

True swarming requires resilient, collaborative, autonomous problem-solving at machine speed.¹ In a genuine swarm, there is no single point of failure; the entity operates as a singular cohesive unit rather than a plural collection of independent platforms.¹ The U.S. defense industry has largely failed to deliver distributed systems for useful, resilient, collaborative swarming behavior, instead focusing on producing large quantities of individual airframes. By characterizing groups of their products as “swarms,” defense contractors have confused customers and blunted the demand signal that should be fostering a breakthrough capability in distributed systems architecture.¹

Current U.S. approaches to multi-drone operations typically utilize a “one-to-many” model.¹ In this model, multiple drones maneuver in sync under the direction of a central processor or a single human operator utilizing pre-scripted formations. These centralized systems are highly vulnerable; if the leader node is compromised, jammed, or destroyed, the entire group fails.¹ Furthermore, they lack the ability to dynamically adapt at machine speed if the tactical situation changes unexpectedly.

Cloud Independence and Consensus-Based State Management

To achieve true swarming, individual drones must operate on a distributed systems infrastructure. This requires constant, decentralized consensus-building among individual nodes to maintain a shared Common Operating Picture (COP).¹ The drones must continuously agree on the state of the world, target assignments, and navigational hazards without relying on a central server.

A significant challenge in modernizing this capability is the reliance on cloud provider dependencies. Modernization professionals often design systems that require high-bandwidth connections to centralized cloud servers.¹ However, military operators in contested environments cannot rely on uninterrupted high-bandwidth connectivity due to adversarial electronic warfare and spectrum jamming.¹ True swarms must be cloud-independent, utilizing locally self-contained software infrastructure to coordinate via ad hoc, short-range connections that can self-heal dynamically when nodes drop out.¹

The Implications for Testing and Evaluation

The architectural shift toward distributed systems renders legacy testing paradigms obsolete. Evaluating a true swarm requires testing the communication protocols, routing logic, and consensus algorithms that allow multiple autonomous systems from potentially different vendors to function as a cohesive team.²¹ The(https://www.cnas.org/press/press-release/cnas-releases-new-report-lessons-in-learning-ensuring-interoperability-for-autonomous-systems-in-the-department-of-defense) emphasizes that unlike human pilots who can rely on informal coordination over radio, autonomous systems must use preprogrammed, shared protocols.²¹

Legacy ranges lack the instrumentation to effectively monitor, record, and evaluate the internal logic and network state of hundreds of distributed nodes operating simultaneously. If a physical swarm fails to execute a mission, determining whether the failure was caused by a hardware sensor malfunction, network packet loss due to natural atmospheric interference, or a logical flaw in the decentralized consensus algorithm is exceptionally difficult without robust synthetic telemetry mirroring the event.¹ Therefore, the evaluation of interoperability and swarm communication constitutes a primary bottleneck in the current TEVV enterprise.

4. Inadequacies of Legacy Physical Test Ranges

The Director, Operational Test and Evaluation (DOT&E) has repeatedly emphasized that the DoD must rapidly and rigorously test its systems across contested domains to determine operational effectiveness, survivability, and lethality.²³ While traditional physical test ranges remain vital for final operational validation and flight qualification, they present severe bottlenecks for the iterative development of non-deterministic AI and autonomous swarms.³

Geographic Constraints and Safety Footprints

Physical test ranges are geographically bounded and heavily regulated by civilian aviation authorities, such as the Federal Aviation Administration (FAA), and stringent range safety protocols. The testing of unmanned aircraft systems must adhere to strict guidelines regarding population density categories and operational boundaries.²⁴ Testing a drone swarm intended to simulate hundreds of interconnected autonomous munitions requires massive expanses of unencumbered airspace and electromagnetic spectrum.

Physical ranges must manage strict safety footprints to ensure that an anomalous algorithm does not cause an autonomous vehicle to breach civilian airspace or cause damage to property and life.²⁵ As the role of unmanned systems expands, external sensor suites grow more complex, and data processing accelerates, the forces pushing the limits of safe human operational command and control introduce significant risks to physical testing.²⁵ Implementing Sense and Avoid (SAA) systems in coordination with standard aviation protocols requires careful physical bounding that artificially limits the parameters under which an AI can be tested.²⁶

The Logistical Burden of Contested Environment Replication

Replicating the dense, contested environments of modern warfare is practically impossible in the physical world at scale. Establishing a realistic test environment requires deploying complex arrays of adversarial surface-to-air missile (SAM) simulators, GPS spoofing equipment, multi-spectral camouflage, dynamic moving targets, and dense electronic warfare interference.³ The logistics, fuel costs, personnel requirements, and maintenance overhead required to coordinate a single live-fly event with these assets are astronomical. Consequently, this limits testing frequency to a handful of highly scripted, tightly controlled events per year, which is entirely insufficient for software development.

The Iteration Deficit in Machine Learning

The most significant limitation of physical ranges is the iteration deficit. The development of capable AI agents relies on reinforcement learning (RL), a process where an algorithm learns optimal behavior through trial and error over millions of iterations.⁴ In a simulated environment, an autonomous agent can fight a million dogfights in a single day, exploring different tactical geometries, reacting to dynamic threats, and learning from its failures.⁴

If the DoD relies primarily on live-flight testing, an AI model might experience only a few dozen engagements per month. This slow feedback loop is incompatible with modern software development life cycles and the pace of adversarial technological advancement. As highlighted by(https://shield.ai/autonomy-for-the-world-x-62-vista/), combat-ready AI agents demand continuous training around the clock in simulation environments before they are ever loaded onto a physical airframe for validation.⁴ Relying on physical ranges for the bulk of algorithmic training fundamentally throttles the speed of innovation.

[Insert image: A visual contrasting the linear, constrained nature of physical testing against the high-volume, limitless iterations of synthetic simulation environments.]

5. Modernizing the TEVV Ecosystem through Synthetic Simulation

To overcome the iteration deficit of physical ranges and establish the statistical confidence required for deployment, the DoD must heavily invest in modernizing simulation environments. Synthetic range capability is not merely an alternative to live testing; it is the foundational prerequisite for developing credible autonomous systems.³ Development and assessment of these systems must be accelerated with credible synthetic range capabilities that support hardware-in-the-loop (HITL) and software-in-the-loop (SITL) evaluation within operationally representative conditions.³

The Role of Digital Twins and Synthetic Data

Digital engineering is rapidly becoming a standard practice across DoD acquisition and sustainment, embedding virtual-first approaches into lifecycle management as directed by DoD Instruction 5000.97.²⁸ Central to this shift is the creation of digital twins. A digital twin is a high-fidelity virtual representation of a physical object, process, or environment that mirrors its real-world counterpart to predict future behavior, powered by real-time data inputs.²⁹

In the context of drone TEVV, digital twins allow engineers to run virtual stress tests, environmental simulations, and edge-case scenarios before physical prototypes are even constructed, thereby catching design flaws early and reducing physical prototyping costs.²⁸ For example, the U.S. Army recently contracted Duality AI to utilize its Falcon digital twin simulation platform to develop an AI-based anti-drone detection system.³⁰ By generating massive volumes of synthetic data representing diverse adversarial environments, digital twins provide the fuel necessary for reinforcement learning engines, enabling faster and more cost-effective deployment through a digital-first approach.³⁰

Case Study: Aerospace Autonomy and the VISTA X-62A

The U.S. Air Force’s X-62A Variable In-Flight Simulation Test Aircraft (VISTA) represents the gold standard for bridging synthetic simulation with live-flight validation. Developed by Lockheed Martin Skunk Works in collaboration with Calspan Corporation for the U.S. Air Force Test Pilot School, VISTA is a heavily modified F-16 utilizing an open systems architecture.³² This architecture allows the aircraft to mimic the aerodynamic performance characteristics of other airframes and host non-vendor-locked third-party AI applications.³²

VISTA is a critical asset because it enables the physical validation of algorithms trained in synthetic environments. In a recent milestone, an autonomous AI agent developed by Shield AI took control of the VISTA and executed tactical basic fighter maneuvers (dogfighting) against a human-piloted F-16.⁴ The success of this live test was entirely predicated on the millions of daily synthetic dogfights the AI agent executed in simulation.⁴ VISTA provides the crucial hardware-in-the-loop validation, proving that an algorithm optimized in a sterile digital environment can handle the latency, sensor noise, and dynamic aerodynamic realities of physical flight—operating at speeds up to Mach 2 and altitudes of 50,000 feet—without requiring the procurement of a dedicated, single-purpose test drone.⁴

Case Study: Maritime Autonomy and the Naval Autonomous Test System

In the maritime domain, testing autonomous unmanned surface vehicles (USVs) introduces unique complexities. USVs must comply with collision regulations, navigate complex wave dynamics, and maneuver through shallow waterways.³⁶ To address this, the Navy and academic partners are developing the Naval Autonomous Test System (NATS).³

NATS is a simulation framework built on platforms like Unity, MATLAB-Simulink, and ROS2 (Robot Operating System).³⁶ It creates digital twins of the real-world maritime environment, allowing for the evaluation of autonomous navigation algorithms through rigorous software-in-the-loop (SITL) testing.⁸ The framework models the complex interactions between a vessel’s control algorithms and realistic environmental factors, generating three-dimensional navigation environments by combining actual wave spectra with Gerstner waves.³⁸ By utilizing high-resolution bathymetric data from major U.S. ports, NATS can test a USV’s ability to navigate confined waterways and avoid grounding—scenarios that are too dangerous and costly to test iteratively with physical vessels.³⁶ This framework provides a simulation that encompasses the challenges of complex maritime operations, assisting developers in discovering unpredicted interactions and improving system robustness.⁸

Quantifying Swarm Performance in Synthetic Environments

When testing massive drone swarms in these synthetic environments, traditional platform-centric performance metrics must be adapted. Swarm TEVV requires tracking distributed behaviors, evaluating how effectively algorithms balance speed, solution quality, scalability, and reliability.³⁹

Key metrics evaluated in simulation include:

• Convergence Speed: The measure of how quickly the decentralized algorithm finds a solution or reaches consensus among nodes, quantified by tracking computation time or the number of iterations needed to reach a predefined error threshold.³⁹
• Solution Quality: Measured using error rates, fitness values, or comparisons to ground-truth solutions (e.g., using standardized benchmark test functions like Rastrigin or Schwefel) to ensure the swarm selects optimal paths or targets.³⁹
• Scalability: Evaluated by increasing the problem size—such as adding hundreds of new drones to the network—and observing performance degradation to ensure the swarm maintains coordination under load.³⁹
• Robustness and Fault Tolerance: The swarm’s ability to adapt to dynamic constraints, tested by introducing synthetic sensor noise, simulating node attrition, or altering constraints mid-execution and measuring success rates across multiple runs.³⁹
• Formation Integrity and Leadership Error: The ability of the swarm to maintain spatial coherence under varying degrees of communication latency and positioning errors.²⁷

[Insert image: A structured table outlining the core metrics used to evaluate autonomous swarm performance within synthetic simulation environments.]

6. The Administrative Stranglehold: Safety Review Boards and Certification

Beyond the physical limitations of test ranges, the DoD’s administrative and safety certification apparatus presents a severe bottleneck. Before any weapon system can be deployed, it must be evaluated by Safety Review Boards (SRBs) and certification agents to ensure it operates within acceptable risk parameters. For legacy systems, this is achieved through established systems engineering processes and a rigid adherence to Level of Rigor (LOR) standards.⁵

The Failure of Level of Rigor (LOR) for Machine Learning

The Office of the Under Secretary of Defense for Research and Engineering (OUSD(R&E)) has explicitly identified that applying traditional LOR to machine learning is fundamentally insufficient to mitigate risk.⁵ In traditional software engineering, SRBs rely on extensive documentation that provides a deep understanding of implemented behavior. Engineers conduct low-level design hazard analyses, code-level hazard analyses, and Requirements-Based Structural Coverage Analysis at the Modified Condition/Decision Coverage (MC/DC) level to guarantee that every line of code executes as intended, evaluating data and control coupling.⁵

Machine learning models, however, function effectively as “black boxes.” They lack the transparency required for traditional software safety means, making it impossible to create the artifacts that map how a neural network weighs billions of parameters to arrive at a specific target identification.⁵ Consequently, SRBs are left evaluating a system without the deep analytical insight they have historically relied upon. For high-criticality tasks, known as Safety Flight Critical Index 1 (SFCI 1) functions, the uncertainty associated with ML precludes the ability to provide sufficient confidence based solely on developmental assurance and LOR.⁵

The Operational Design Domain (ODD) Mismatch

A fundamental challenge for SRBs evaluating autonomous systems is the inherent misalignment between the Operational Design Domain (ODD) and the Training Data Distribution.⁵ The ODD defines the specific conditions under which a system is designed to function (e.g., specific altitudes, weather conditions, threat landscapes). The training data represents the dataset used to teach the AI how to operate within that ODD.

By the nature of machine learning, the training data will always be a limited sample or subset of the infinite complexities of the real-world ODD.⁵ While developers strive for robust generalization, the real world will always present edge cases—novel visual patterns, unpredictable adversary tactics, or anomalous sensor inputs—that were absent from the training set. This guarantees a remaining margin of uncertainty and an expected lower success rate in operational deployment than in controlled testing.⁵ SRBs, traditionally tasked with eliminating uncertainty, struggle to certify systems where residual uncertainty is an architectural reality.

Automation Bias and the Challenge of Explainability

When SRBs evaluate decision-support systems (AI-DSS)—such as AI designed to filter reconnaissance data and recommend targets for human commanders—they face the challenge of evaluating human-machine teaming. AI models can suffer from subtle failure modes; they may act deceptively, tell human operators what they want to hear based on warped incentives, or generate hallucinations under battlefield conditions.¹⁹

The lack of robust explainability limits the ability of operators to verify the reasoning behind an AI’s target recommendation.⁵ Empirical evidence from recent conflicts indicates that utilizing AI-DSS to accelerate phases within the joint targeting cycle risks encouraging over-reliance on unverified outputs (automation bias), potentially exacerbating civilian harm rather than preventing it.¹⁴ If SRBs mandate perfect explainability before deployment, AI acquisition will stall indefinitely, as current technical capabilities in ML explainability are far off from providing commensurate insight.⁵

Operationalizing Unmanned Systems Safety Precepts

To navigate these challenges, leadership must guide SRBs to accept new frameworks of governability. The Unmanned Systems Safety Guide for DoD Acquisition outlines specific safety engineering precepts categorized into Programmatic, Operational, and Design Safety Precepts (PSP, OSP, DSP).²⁵ These precepts assist program managers in mitigating hazards unique to unmanned capabilities.

State-of-the-art mitigations for managing machine learning include the use of deterministic checkpoints within software architecture, which provide run-time assurance that the autonomous function does not exceed defined safety limits.²⁵ Additionally, implementing strict bounding of autonomous functions—such as physical/temporal bounds and enforcing human-in-the-loop or human-on-the-loop oversight—reduces risk.²⁵ SRBs must be trained to evaluate systems based on reliable containment, operator override mechanisms, and statistical performance boundaries rather than demanding perfect code transparency.

7. Shifting Paradigms in Cybersecurity: The cATO Initiative

If the DoD successfully modernizes its physical and synthetic testing environments, it will generate highly capable AI models at unprecedented speeds. However, if these models must pass through the legacy cybersecurity and deployment authorization pipelines, the strategic advantage of rapid iteration will be lost. To address this, the Department is undertaking a paradigm shift in how software and AI updates are approved for operational use.

The Limitations of Traditional ATOs

Historically, defense software requires an Authorization to Operate (ATO) certification. The ATO process relies heavily on the Risk Management Framework (RMF) and involves point-in-time, document-heavy technical security assessments.⁹ Securing an ATO is notoriously slow, rigid, and resource-intensive. A program office might spend months generating the required administrative paperwork to prove compliance, securing approval for a system that could face entirely new cyber vulnerabilities six months later.¹⁰

For autonomous drones relying on AI, a static ATO is highly detrimental. Adversarial tactics evolve daily; an AI model trained to recognize enemy assets must be continuously updated with new data to remain relevant.⁴¹ As former Acting DoD CIO Katie Arrington noted, relying on software within a static ATO environment fails the warfighter because the operational environment and the adversary are constantly dynamic.⁴¹ If every model retrain or software patch triggers a multi-month ATO recertification process, the drone swarm will always be fighting with outdated intelligence.

Transitioning to Continuous Authority to Operate (cATO)

To enable the rapid, secure deployment of software updates, the DoD is implementing the Continuous Authority to Operate (cATO) framework. The DoD defines cATO as a state achieved when an organization that develops, secures, and operates a system demonstrates sufficient maturity in its ability to maintain a resilient cybersecurity posture, rendering traditional risk assessments and authorizations redundant.⁶

Under the cATO framework, the focus shifts from evaluating a static piece of software to evaluating the maturity of the software factory that produces it.⁹ If a DoD software delivery organization utilizes approved DevSecOps platforms that meet DoD Enterprise DevSecOps Reference Designs, implements continuous risk monitoring, and practices active cyber defense, it can be granted a cATO.⁹ This authorizes the organization to continuously develop, assess, and deploy software updates directly to the field—including pushing new AI models to operational drones—without awaiting secondary administrative approvals, provided the updates remain within the established risk tolerances.⁹

Programs like the Software Fast Track (SWIFT) initiative are actively seeking to replace legacy ATO mechanisms with automated, AI-driven risk assessments, doing third-party assessments of companies’ cybersecurity postures based on defined risk criteria.⁴¹ The U.S. Army has already begun applying the cATO framework to existing systems like Nett Warrior and Gabriel Nimbus, marking a fundamental cultural shift from compliance-based administration to threat-based continuous risk management.⁶ For drone TEVV, securing a cATO for autonomy software factories is a critical prerequisite for maintaining tactical agility.

8. Maturing the Evaluation Standard: Transitioning to the AI Readiness Framework

A core administrative mechanism utilized by the DoD to manage defense acquisitions is the Technology Readiness Assessment (TRA), which relies heavily on Technology Readiness Levels (TRLs).⁴³ Originally developed by NASA in the 1970s and formally endorsed by the DoD in 2001, the 1-to-9 TRL scale was designed to gauge the maturity of hardware systems.⁴³

The Failure of TRLs for AI Capabilities

The TRL framework measures progression from basic observed principles (TRL 1) to analytical proof of concept (TRL 3), component validation in a laboratory (TRL 4), prototype demonstration in a relevant environment (TRL 6), and finally, successful system operations in combat (TRL 9).⁴⁴ While TRLs are highly effective for evaluating the structural maturity of a drone’s airframe, propulsion system, or sensor hardware, they are fundamentally inadequate for evaluating the maturity of the AI algorithms controlling the drone.¹¹

Current technology readiness assessments fail to capture critical AI-specific risk factors, such as data integrity, algorithmic bias, model drift, and the quality of human-machine interaction.¹¹ A traditional TRL assessment assumes a linear development path where a component works identically in a high-fidelity lab environment as it does in an operational setting.⁴³ As previously established, the non-deterministic nature of AI and the mismatch between training data and the operational environment means this linear assumption is false. An AI model that exhibits flawless target recognition on a simulated range may fail completely when encountering novel weather patterns or adversarial camouflage in the field.⁵

The Proposed AI Readiness Framework (AIRL)

To ensure justified confidence in AI-enabled systems prior to deployment, the DoD and associated policy experts are advocating for the adoption of a dedicated AI Readiness Framework, analogous to but expanded beyond traditional TRLs.¹¹ This framework provides decision-makers with a multidimensional view of an autonomous system’s maturity, acknowledging that readiness requires organizational commitment and the addressing of skills and capability gaps.⁴⁷

A comprehensive AI Readiness Framework requires evaluating several core pillars that go beyond mere software functionality:

• Justified Confidence and Alignment: Ensuring the AI system’s probabilistic outputs are tightly aligned with commander intent and rules of engagement, and that performance degradation in edge cases is statistically quantified and understood.¹¹
• Data Readiness Level (DRL): Assessing the maturity, security, and representativeness of the data used to train the algorithm. A highly advanced algorithm trained on low-quality, incomplete, or biased data has a low DRL and represents a severe operational risk.¹¹
• Human Readiness Level (HRL): Evaluating the interface between the AI and the operator. This measures whether the system is understandable, whether operators are sufficiently trained to recognize algorithmic hallucination or failure, and whether effective override mechanisms (governability) are in place to prevent automation bias.¹¹
• Governance and Continuous Benchmarking: The establishment of standardized AI safety benchmarks and monitoring protocols to track performance gaps over time.¹¹ A federally coordinated benchmarking hub, spearheaded by entities like the Chief Data and Artificial Intelligence Officer (CDAO) and Defense Innovation Unit (DIU), is critical for delivering uniform evaluations across the DoD.⁴⁹

Adopting this expanded framework, alongside utilizing tools like the CDAO’s Pathway to AI Readiness and AI Readiness Assessment (AIRA) metrics, allows acquisition professionals to communicate the true readiness of an autonomous system to operational commanders.⁵⁰ This ensures that the deployment of AI is based on comprehensive risk awareness rather than hardware milestones alone.

9. Acquisition Dynamics and Resource Allocation Bottlenecks

The structural issues within TEVV are exacerbated by systemic flaws in defense acquisition protocols. The DoD system acquisition process often outsources software development to contractors while limiting input from military end-users, leading to systems that fail to meet operational realities.¹⁸

When the DoD relies entirely on commercial defense contractors to provide the testing environments and validate their own autonomous systems, it risks vendor lock-in and excessive cost overheads. Investigations into defense contracting have repeatedly highlighted issues with pricing data validation; for example, the DoD inspector general has previously found contractors overcharging the military by vast margins for basic spare parts due to loopholes in the Truth in Negotiations Act (TINA).⁵³ If these same opaque pricing models are applied to proprietary software simulation environments and AI training algorithms, the cost of modernizing drone swarms will become unsustainable.

To mitigate this, the DoD must retain ownership of the testing infrastructure and enforce open systems architectures. By mandating that contractors utilize government-owned digital twins and standardized benchmarking frameworks managed by the CDAO, the DoD can ensure competitive pricing, prevent siloed development efforts, and maintain rigorous, unbiased oversight over the algorithms being integrated into the Joint Force.²¹

10. Strategic Recommendations for DoD Leadership

The transition to an AI-enabled, autonomous Joint Force is not merely an engineering challenge; it is fundamentally an infrastructural and regulatory challenge. To overcome the existing TEVV bottlenecks and realize the strategic potential of drone swarms, DoD leadership must operationalize the following recommendations:

1. Reallocate Funding to Synthetic T&E Infrastructure

Current acquisition budgets heavily favor platform procurement. Leadership must direct significant, sustained funding toward the development of enterprise-wide digital twins, realistic synthetic data generators, and joint simulation frameworks (analogous to the Naval Autonomous Test System and VISTA X-62A capabilities). Autonomous systems cannot mature without the capacity to conduct millions of daily reinforcement learning iterations in high-fidelity, adversarial digital environments.

2. Demand Distributed Systems Architectures for Swarms

Acquisition executives must refine their demand signals to industry. Solicitations for drone swarms must explicitly require cloud-independent, distributed systems architectures capable of localized consensus building. Procuring vast quantities of remotely piloted drones and categorizing them as a “swarm” dilutes the capability and perpetuates vulnerabilities associated with centralized single points of failure.

3. Accelerate the Transition to Continuous Authority to Operate (cATO)

The traditional Authorization to Operate (ATO) process is incompatible with the operational tempo required for AI software updates. Leadership must support the DoD CIO’s efforts to implement cATO frameworks across all autonomous systems program offices. Cultivating mature DevSecOps software factories allows for the continuous, secure deployment of refined algorithms directly to the tactical edge without bureaucratic delay.

4. Evolve Safety Review Board (SRB) Methodologies

SRBs must be given the doctrinal authority and the technical tools to evaluate non-deterministic systems. Leadership should issue guidance formally recognizing that legacy Level of Rigor (LOR) standards are insufficient for machine learning. SRBs must shift toward evaluating statistical confidence limits, implementing deterministic checkpoints, and enforcing human override mechanisms, acknowledging that some level of residual uncertainty is inherent to AI.

5. Adopt the AI Readiness Framework (AIRL)

The DoD should formally expand its acquisition taxonomy by integrating AI Readiness Levels alongside traditional Technology Readiness Levels. By establishing distinct, enforceable metrics for Data Readiness Levels (DRL) and Human Readiness Levels (HRL), decision-makers will gain an accurate, comprehensive assessment of an autonomous system’s true combat readiness, ensuring that human commanders can trust the tools they deploy.

The successful deployment of autonomous military systems hinges not on the physical sophistication of the drone itself, but on the rigor, scale, and agility of the digital systems used to test, evaluate, and certify it. Modernizing the TEVV enterprise is the indispensable prerequisite for maintaining technological overmatch in future conflicts.

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1. Executive Summary

The United States Department of Defense (DoD) is undertaking a profound structural transition in force design, characterized by rapid, massive investments in scaled autonomous systems. Initiatives such as the Replicator program, which seeks to field thousands of all-domain attritable autonomous (ADA2) systems by late 2025, alongside recent executive directives intended to unleash domestic drone manufacturing, indicate a strategic pivot toward mass and numerical advantage.¹ However, current strategic dialogues and procurement focuses heavily favor the acquisition of physical airframes, payloads, and autonomous navigation software, while the systemic requirements—specifically the invisible architecture of the electromagnetic spectrum (EMS)—remain critically underexamined.³

Operating thousands of uncrewed systems simultaneously generates extreme bandwidth limitations, heightens the risk of friendly electromagnetic interference (blue-on-blue EMI), and creates severe vulnerabilities to adversarial electronic warfare (EW).³ The historical reliance on clear, centralized communication lines is a fundamental vulnerability in contested environments.⁵ To successfully enable warfighters and employ drones effectively, DoD leadership must shift its conceptual framework from viewing the EMS as a passive, guaranteed utility to recognizing it as a highly contested, primary maneuver space.⁵

This strategic report provides an exhaustive overview of the systemic requirements necessary to design, build, operate, and evolve scaled drone architectures. It outlines the limitations of current bandwidth allocations, details the adversarial electronic warfare threat landscape based on recent combat observations in theaters such as Ukraine, and provides an actionable framework for operational resilience.⁷ The accumulated evidence suggests that operational viability in contested environments requires abandoning centralized “mothership” command structures in favor of decentralized, self-healing mesh networks.¹⁰ Furthermore, forces must push computational processing to the tactical edge to drastically reduce bandwidth dependencies and adopt software-defined, modular open systems architectures (MOSA) to enable real-time frequency agility and stealth communications.¹²

2. The Strategic Imperative: The Electromagnetic Spectrum as a Primary Maneuver Space

For decades, military communications architectures were conceptualized and built around the fundamental assumption that spectrum access could be predicted, allocated, and largely preserved through operational planning.⁵ Frequency plans, static allocations, and pre-mission deconfliction procedures functioned effectively in environments where spectrum access remained orderly and enforceable by allied forces.⁵ That operational context no longer exists. Contemporary military deployments unfold amid dense, unpredictable, and overlapping radio frequency (RF) emissions stemming from military, commercial, and civilian activities.⁵

2.1. The Shift to Electromagnetic Spectrum Operations (EMSO)

Strategic competitors and adversaries have thoroughly observed the deep reliance of United States forces on EMS-dependent capabilities and have actively structured their forces to exploit this specific vulnerability.¹⁵ Consequently, without control or resilience within the EMS, precision capabilities degrade, joint operations falter, and broader deterrence erodes.¹⁵ Recognizing this paradigm shift, the DoD formalized a new reality in its 2020 Electromagnetic Spectrum Superiority Strategy. This document marked a transition from the traditional concept of electronic warfare (EW)—often viewed as a secondary or supporting capability—into the broader, unified framework of Electromagnetic Spectrum Operations (EMSO).¹⁷

Despite this critical doctrinal shift, execution and bureaucratic alignment have historically lagged. Assessments by the Government Accountability Office (GAO) have noted that the Department risks failing to achieve its spectrum strategy goals due to a lack of clearly identified processes, reformed governance structures, and assigned leadership for strategy implementation.¹⁸ Accountability mechanisms and oversight processes required to integrate spectrum operations across the joint force must be accelerated to match the pace of physical drone acquisition.¹⁸

2.2. Domestic Spectrum Scarcity and Commercial Contention

Compounding the tactical threat is a broader, systemic issue of spectrum scarcity. Spectrum is a finite national and international resource. Treaties and international agreements govern global allocations, making it exceedingly difficult to acquire new spectrum permanently for military use.³ Domestically, significant portions of the bandwidth previously reserved for the exclusive use of the DoD have been auctioned to private entities over the past decade to form the foundation of commercial 4G and 5G cellular networks.²⁰

This commercial sell-off restricts the DoD’s freedom of action and means that large-scale drone deployments cannot simply be assigned dedicated, pristine frequencies.¹⁷ Furthermore, many systems are acquired as Commercial Off-The-Shelf (COTS) products tested using U.S. commercial spectrum; when transitioned to military use, these systems often face priority conflicts or are prohibited from use outside the United States unless transferred to military-use spectrum.³ Future operations therefore demand dynamic spectrum sharing and cognitive technologies that allow military systems to coexist within congested commercial and contested tactical bands.²⁰ The DoD’s Office of the Chief Information Officer and the FutureG office are actively exploring dynamic spectrum-sharing demonstrations to allow the Pentagon and private sector to simultaneously utilize the same spectrum bands without degradation.²¹

3. The Ambition and Reality of Scaled Autonomous Systems

The DoD’s current acquisition trajectory reflects an urgent drive to overcome the numerical advantages of adversaries through the mass deployment of uncrewed systems. However, a significant disconnect exists between the procurement of these physical assets and the maturation of the networks required to sustain them.

3.1. The Replicator Initiative and ADA2

Launched to counter the mass of the Chinese military, the Replicator Initiative represents a high-profile effort to rapidly field thousands of autonomous systems across multiple domains within an aggressive timeline.¹ Managed by the Defense Innovation Unit (DIU), Replicator bypasses traditional acquisition programs to accelerate the fielding of all-domain attritable autonomy (ADA2).¹ These are low-cost, expendable platforms designed to be lost in combat.¹ Replicator 1 focuses on fielding thousands of uncrewed aerial, ground, maritime, and space-based systems by August 2025, while Replicator 2 targets counter-small unmanned aerial systems (C-sUAS).¹

While the strategic intent is clear, the initiative faces scrutiny regarding technological integration. Congressional Research Service (CRS) analyses have raised oversight concerns framing Replicator as a test case for future defense innovation, warning that accelerated fielding may pose risks to system reliability, interoperability, and long-term sustainment.¹ Crucially, the initiative’s success relies heavily on unnamed software vendors tasked with enabling swarming, autonomous navigation, and dynamic threat response—all of which require a robust, resilient electromagnetic backbone.¹

3.2. Executive Directives and the Procurement Drive

The push for mass has been further codified by recent executive actions. Executive Order 14307, issued to support the American drone industry and arm warfighters, mandates that the DoD must be able to procure, integrate, and train using low-cost, high-performing drones manufactured domestically.² Follow-on memorandums from DoD leadership have emphasized “Unleashing U.S. Military Drone Dominance,” rescinding restrictive policies to power a technological leapfrog.²

This directive acknowledges that adversaries collectively produce millions of cheap drones annually, and U.S. units must be outfitted with lethal small drones.² However, as procurement scales rapidly, the systemic infrastructure must scale concurrently. Building millions of drones yields no tactical advantage if they cannot communicate, coordinate, or survive in a contested electromagnetic environment. The focus must expand from simply acquiring platforms to establishing the network architectures that make those platforms lethal and survivable.⁵

4. Physical and Infrastructural Vulnerabilities of Mass Operations

The ambition to field thousands of autonomous systems introduces immediate physical and infrastructural limitations. Leadership must recognize the nonlinear complexity that arises when scaling from single-drone operations to coordinated swarms. The primary constraints are bandwidth saturation, signal latency, and internal electromagnetic interference.

4.1. Bandwidth Saturation and Latency Constraints

Uncrewed Aircraft Systems rely heavily on datalinks for command and control (C2), telemetry, and the transmission of sensor payloads. As the density of drones increases within a given airspace, the demand for spectrum rapidly outpaces availability.³ High-fidelity sensors designed for intelligence, surveillance, and reconnaissance (ISR) or target acquisition generate massive, continuous data streams.³ In recent tactical counter-UAS exercises, such as Project Flytrap in Germany, single U.S. Army platoons found that 70 percent of their available tactical bandwidth was consumed entirely by sensor data alone.²³

Beyond Visual Line of Sight (BVLOS) operations, which are essential for long-range missions and deep strikes, compound this communication challenge.²⁴ Drones operating at extended ranges face severe signal attenuation—often exceeding 120 decibels at distances beyond 50 kilometers.²⁵ To maintain connectivity, systems frequently rely on satellite communications (SATCOM). While SATCOM offers global coverage and bypasses local terrestrial obstacles, it introduces substantial propagation delays, with latency ranging from 500 milliseconds to over a full second.²⁴ In the context of high-speed drone swarms, autonomous coordination, and kinetic fire control, milliseconds matter.²³ High latency can cripple real-time swarm coordination and render rapid targeting impossible.

4.2. Blue-on-Blue Electromagnetic Interference (EMI)

When thousands of electronic devices operate in close proximity, they generate significant RF noise, leading to internal or “blue-on-blue” electromagnetic interference.²⁶ Failure to properly shield and coordinate drones can result in disrupted flight controls, severed communication links, and mid-mission platform failure without any enemy action.²⁸

Internal EMI in swarms stems from multiple sources. Propulsion system electrical noise, the physical overlap of wireless modules, and the proximity of unshielded cables all contribute to a chaotic internal electromagnetic environment.⁴ As the swarm scales, coordination complexity increases non-linearly. Field tests demonstrate that communication latency between drones can spike, and positioning errors can accumulate rapidly—at rates of 2 to 5 centimeters per minute—especially in GPS-denied scenarios.³⁰

To mitigate these physical vulnerabilities, system designs must incorporate rigorous electromagnetic compatibility (EMC) standards and active coordination techniques.

5. The Adversarial Threat Landscape: Electronic Warfare and Contested Environments

The operating environment defined by current near-peer competitors is actively hostile to traditional RF communications. Insights derived from ongoing conflicts, particularly in Ukraine, demonstrate that numerical mass alone does not guarantee tactical success if the overarching network is highly susceptible to adversarial electronic warfare.³⁵ In modern combat, the drone will only get through if its communication architecture survives.³⁷

5.1. The Russian Electronic Warfare Architecture

Russian military doctrine heavily integrates non-kinetic spectrum operations across all service branches.³⁸ Since 2009, the vertical integration of domestic defense industry companies into entities like the Radio-Electronic Technologies Concern (KRET) has driven intensive lobbying and promotion of EW interests within the Russian military.³⁸ Consequently, the Russian Armed Forces deploy sophisticated, layered EW systems designed specifically to suppress satellite navigation, disrupt tactical communications, and neutralize drone operations.⁷

Key adversarial systems observed in current theaters include:

The lethality of these systems is evident in the severe attrition rates of tactical drones. In highly contested zones, 60 to 80 percent of all first-person view (FPV) drones operated have been neutralized by RF and GNSS signal jamming.⁹ To circumvent this overwhelming electromagnetic pressure, forces have occasionally been forced to revert to physical workarounds. For example, innovations have included deploying FPV drones tethered by kilometers of physical fiber-optic cables, allowing them to penetrate areas of heavy jamming and strike targets without relying on the electromagnetic spectrum at all.⁹ While effective in niche tactical scenarios, tethered systems cannot scale to meet the strategic requirements of ADA2 or broad swarm operations.

5.2. Advanced Adversarial Swarm Capabilities

Strategic competitors are concurrently developing their own uncrewed capabilities and counter-swarm technologies. The People’s Liberation Army (PLA) has heavily prioritized the “intelligentization” of its forces, accelerating drone warfare research for potential multi-domain conflicts, including specific testing for scenarios involving an invasion of Taiwan.⁴² Analysis indicates that Chinese development in artificial intelligence for drone swarms is rapidly advancing, focusing on utilizing uncrewed systems to overwhelm adversary air defenses.⁴²

However, intelligence assessments also note vulnerabilities in adversary AI development, such as the risk of “model collapse.” As Chinese leaders deploy AI systems trained on strictly controlled domestic information ecosystems, the models become detached from ground truth, functioning merely as mirrors of the state’s information control apparatus rather than objective intelligence processors.⁴³

Concurrently, foreign militaries are closely studying western doctrine to develop automated, AI-driven counter-drone architectures. In response, the U.S. Army is accelerating initiatives like Project Golden Shields, designed to automate the response chain against high-volume aerial threats and drone swarms.⁴⁴ These automated defense systems will undoubtedly become high-priority targets for adversaries, further emphasizing the need for resilient EMS operations.

6. Decentralized Command and Control: Transitioning to Leaderless Swarms

The DoD’s historical reliance on centralized command-and-control (C2) models presents a critical vulnerability in the modern electromagnetic environment. Legacy drone operations typically utilize a “mothership” model or a single ground control station, routing all telemetry, commands, and video feeds through a central node.¹¹

Centralized architectures rely on a single ‘mothership’ or ground station; neutralizing this node incapacitates the entire swarm. In a decentralized mesh network, every drone acts as a router, allowing the swarm to self-heal and maintain operational coherence even if multiple units are jammed or destroyed.¹¹ If an adversary successfully jams, deceives, or physically destroys the mothership, the entire fleet of dependent uncrewed systems is rendered inert, creating an unacceptable single point of failure.⁴⁷

6.1. The Transition to Mesh Networking

To achieve the requisite resilience, communications architectures must shift fundamentally to decentralized, ad-hoc mesh networks.¹⁰ In a true mesh network topology, every drone (or node) functions simultaneously as a transmitter, receiver, and router.⁴⁶ Data packets are not forced through a central hub; instead, they dynamically hop between nodes, automatically evaluating and seeking the most efficient and clear path to their destination based on rules-based criteria.⁴⁶

This architecture provides inherent “self-healing” capabilities. If a drone is shot down, encounters localized jamming, or moves behind physical terrain that blocks line-of-sight, the network instantly re-routes traffic through surviving adjacent nodes.⁴⁶ Technologies such as Kinetic Mesh networking evaluate communication paths in real-time without relying on fixed infrastructure, ensuring that secure communications persist even as platforms move rapidly through highly degraded and dynamic environments.⁴⁹ This is vital for maintaining situational awareness and tracking partner force operations in austere conditions without traditional cellular or SATCOM availability.⁵²

6.2. Leaderless Swarm Autonomy

Software platforms are evolving to manage thousands of assets simultaneously without the need for centralized human control or a single mothership. Systems such as L3Harris’s AMORPHOUS (Autonomous Multi-domain Operations Resiliency Platform for Heterogeneous Unmanned Swarms) are actively demonstrating “leaderless swarm” capabilities.¹¹

In a leaderless swarm, an operator assigns a high-level objective—for example, conducting a search over a specific coordinate grid or executing a coordinated strike. The software distributes the command across the entire fleet.¹¹ The drones intelligently delegate tasks among themselves, deconflict routing to avoid collision and internal EMI, and execute the mission collaboratively.¹¹ This decentralized decision-making allows individual uncrewed assets to perform tasks autonomously inside the network, stripping away the vulnerability of a single, targetable C2 node and enabling rapid scaling across multi-domain operations.⁵³

7. Bandwidth Mitigation through Edge Computing and Sensor Fusion

Solving the bandwidth crisis requires altering not just how data is transmitted, but fundamentally changing what data is transmitted. Traditional architectures stream continuous, raw data—such as high-definition video feeds or unfiltered radar returns—from the drone back to an enterprise cloud or ground station for processing and human analysis. In Denied, Disconnected, Intermittent, and Limited (D-DIL) environments, this reliance on reach-back communications is a fatal flaw.⁵⁵

7.1. Edge Artificial Intelligence

The integration of ruggedized hardware processors—such as Tensor Processing Units (TPUs), Neural Processing Units (NPUs), and Field-Programmable Gate Arrays (FPGAs)—directly onto the drone platform allows for Edge AI.⁵⁵ Edge computing forces the processing of information to occur at the point of collection, far beyond the traditional IT enterprise.⁵⁶

Instead of transmitting gigabytes of high-definition video over a saturated RF link, the drone’s onboard AI processes the video locally. It identifies the target, calculates trajectories, and generates essential metadata.¹² Only this metadata—such as target classification, exact coordinates, and velocity—is transmitted back to the operator or shared with the swarm.¹² This transition from raw data streaming to selective metadata transmission drastically reduces bandwidth requirements, preventing network overload and operator cognitive fatigue.¹²

7.2. Tactical Sensor Fusion

Further network optimization and operational clarity are achieved through advanced sensor fusion. When multiple drones in a swarm detect the same target, legacy systems often transmit separate tracks, clogging the network with redundant data and confusing C2 displays with multiple icons for a single entity.²³

Sensor fusion is the alignment and merging of detections from multiple distributed sensors into a single, highly accurate object track.²³ To achieve this across a swarm, precise temporal alignment is mandatory. All sensors, effectors, and C2 nodes must operate on a strictly synchronized, shared clock.²³ By correlating data at the tactical edge before transmission, the swarm operates as a unified, overcomplete sensory architecture.²³ This ensures that decision-making fidelity is maintained without relying on any single data source and without overwhelming the available tactical spectrum.²³ Precedents for this approach exist in advanced crewed platforms; the F-35, for instance, utilizes a multifunction advanced data link (MADL) to automatically fuse environmental data and distribute a single, unified operational picture across a squadron.⁵⁶

8. Hardware Agility and Stealth Communications

To outmaneuver adversaries in the EMS, the physical hardware of the swarm must be as agile and adaptable as its software. Rigid, single-purpose radio modules are incompatible with the demands of modern electronic warfare.

8.1. Software-Defined Radios (SDR)

Legacy radio modules operate on predetermined frequencies and fixed modulations.⁶⁰ Confronting a new EW threat or adapting to a different spectrum regulatory environment previously required physical hardware replacement, causing severe logistical delays. Software-Defined Radios (SDR) resolve this limitation by shifting the heavy lifting of modulation, demodulation, encoding, and frequency selection from hardware into software.⁶⁰

Cognitive SDRs represent a further leap. These radios can autonomously assess their RF environment, detect interference or jamming, and dynamically adjust frequencies in real-time to optimize performance.⁶⁰ Instead of rigid hardware limitations, SDRs utilize advanced algorithms to instruct the system to hop between frequencies and find innovative pathways to maintain the link.⁶² This flexibility allows a single SDR datalink to support multiple mission profiles, platforms, or even coalition partner networks simply by loading a new software profile, vastly reducing procurement overhead and increasing tactical adaptability.⁶⁰

8.2. Modular Open Systems Approach (MOSA)

To integrate technologies like SDRs and Edge AI rapidly, the DoD is enforcing a Modular Open Systems Approach (MOSA).¹⁴ The implementation of MOSA is not merely a best practice; it is a statutory requirement codified under Title 10 U.S.C. 4401(b).¹⁴ This legislation mandates that all major defense acquisition programs employ modular designs featuring standardized, machine-readable interfaces that allow major components to be incrementally added, removed, or upgraded.¹⁴

By decoupling the radio hardware and sensor payloads from the proprietary flight control software of the airframe, the DoD can continuously field upgraded algorithms from varied commercial vendors to counter evolving EW threats, without having to replace the entire drone fleet.¹³ This open business model permits sharing risk, maximizing asset reuse, and spurring competition among defense and intelligence community partners.¹³

8.3. Low Probability of Intercept and Detection (LPI/LPD)

Survival in a contested spectrum requires operating beneath the adversary’s detection threshold. Traditional omnidirectional radios broadcast signals that are easily detected, pinpointed, and jammed by systems like the Russian Borisoglebsk-2.⁶⁴ Advanced autonomous swarms must utilize Low Probability of Detection (LPD) and Low Probability of Intercept (LPI) communications to remain covert.⁶⁴

• LPD (Low Probability of Detection): Focuses on minimizing the chance an adversary detects the signal’s presence at all. This is achieved utilizing directional transmission, low-power emissions, and noise-like waveforms that blend seamlessly into the background electromagnetic radiation.⁶⁴
• LPI (Low Probability of Intercept): Ensures that even if a signal is detected, it is exceedingly difficult to decode, exploit, or jam. LPI techniques include rapid frequency hopping, complex spread-spectrum modulation, and AI-driven adaptive beamforming.⁶⁴

Implementing advanced LPI/LPD protocols, such as those demonstrated in naval exercises like “Silent Swarm,” within autonomous mesh networks ensures that drones can coordinate tasks, route data, and execute complex maneuvers without broadcasting their position to adversary signals intelligence (SIGINT) operators.⁶⁴

9. Doctrinal, Regulatory, and Organizational Realignments

Technological acquisition must be matched by profound doctrinal and organizational shifts. Procurement of advanced drones is insufficient if the force is not structurally organized, trained, and legally authorized to manage the spectrum those platforms rely upon.

9.1. Decentralized Training and Field Integration

The DoD is currently overhauling its operational doctrine to integrate lessons learned from recent conflicts, shifting aggressively toward a “learn-by-doing” approach.⁶⁷ Initiatives like the Army’s Transformation in Contact (TiC) prioritize deploying new systems directly to operational units rather than waiting years for perfect technological maturity.⁶⁷ This iterative fielding allows real-world user feedback to drive rapid updates to tactics, techniques, and procedures (TTPs).⁶⁷ For instance, recent updates to Field Manual 3-0 include new operational imperatives specifically designed to address persistent drone threats and the necessity of making contact with the smallest element possible.⁶⁷

Furthermore, training regimens must evolve. Operating within the spectrum requires specialized, highly technical knowledge.⁶ Expanding the presence and training of Spectrum Management Officers (SMO) at lower tactical echelons is critical.⁶⁹ These officers are required to deconflict frequencies, navigate highly restrictive Federal Aviation Administration (FAA) and Federal Communications Commission (FCC) regulations, and ensure operational compliance without stifling combat agility.⁶⁹ For example, recent FCC orders enabling limited access to the 5030-5091 MHz band for drone operations require complex registration and deconfliction processes that tactical units must be trained to navigate seamlessly.⁷⁰

9.2. Operationalizing Production at the Tactical Edge

The demands of rapid-reaction warfare expose the inherent limitations of centralized, bureaucratic procurement models. The ability to innovate inside the adversary’s decision cycle requires Fabrication at the Tactical Edge (FATE).⁷² By leveraging additive manufacturing (3D printing) and AI directly in the field, joint forces can design, produce, and modify expendable drone components—such as specific antenna mounts, aerodynamic modifications, or shielding adaptations—in response to immediate EW threats.⁷² This paradigm decentralizes production, effectively allowing forces to execute an acquisition and deployment cycle within 24 hours, rapidly countering localized adversarial spectrum tactics.⁷²

9.3. The Collaborative Combat Aircraft (CCA) Paradigm

The lessons of spectrum resilience being learned in small drone swarms are actively scaling to the highest tiers of air dominance. The U.S. Air Force’s Collaborative Combat Aircraft (CCA) program represents the maturation of these concepts.⁷³ Designed to fly alongside crewed fighters as autonomous wingmen, the CCA relies heavily on secure, semi-autonomous communication architectures.⁷³ Recent successful flights utilizing integrated third-party mission autonomy software (such as Sidekick) integrated via the Autonomy Government Reference Architecture (A-GRA) underscore the viability of decentralized decision-making at higher speeds and larger scales.⁷⁵ However, the ultimate success of programs like the CCA, which may scale to thousands of aircraft, hinges entirely on the exact same principles of EMS resilience, MOSA adherence, and LPI/LPD stealth networking discussed throughout this report.⁷⁴

10. Strategic Conclusions

The Department of Defense’s intense focus on massive drone acquisition signals a necessary and urgent adaptation to the realities of modern warfare. However, the physical hardware of these platforms represents only the visible surface of the capability. The true center of gravity for scaled autonomous operations is the electromagnetic spectrum.

Adversaries are actively fielding sophisticated electronic warfare systems designed specifically to sever the critical links upon which uncrewed systems depend. To secure the operational viability of initiatives like Replicator and broader drone dominance strategies, DoD leadership must prioritize investments in the unseen architecture of the swarm.

This requires an immediate and sustained commitment to moving away from fragile, centralized command structures and embracing self-healing, leaderless mesh networks. It demands the integration of edge computing and sensor fusion to drastically reduce bandwidth dependency and prevent network collapse. Furthermore, the mandatory adoption of software-defined radios and open systems architectures is essential to ensure that U.S. forces can dynamically maneuver within the spectrum faster than adversaries can jam it. Ultimately, success in future conflicts will not be measured solely by the sheer number of drones procured, but by the resilience, agility, and covertness of the networks that connect them.

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1. Executive Summary

The United States Department of Defense is currently undertaking a generational shift in force structure, pivoting aggressively toward the procurement and deployment of thousands of attritable, autonomous unmanned aerial systems. Initiatives such as the Replicator program and the Air Force’s Collaborative Combat Aircraft program reflect a strategic urgency to generate affordable combat mass and offset the quantitative advantages of pacing threats in the Indo-Pacific theater.¹ However, current acquisition and operational frameworks heavily prioritize the technological capabilities and domestic industrial base capacity required to build these systems, frequently overlooking the systemic, forward-edge logistical requirements necessary to sustain them in highly contested environments.¹

This report provides a strategic evaluation of the sustainment vulnerabilities inherent in the deployment of highly expendable drone fleets. The central thesis indicates that treating attritable systems with legacy, slow-moving depot-level maintenance frameworks will result in operational failure when supply lines are severed. In environments characterized by Agile Combat Employment and persistent multi-domain threats, combat units cannot afford the extended turnaround times typical of traditional aviation maintenance.⁵ The margin for error in combat has narrowed significantly, and generating continuous combat power relies entirely on the ability to repair, adapt, and relaunch unmanned systems from austere locations under active threat.⁸

To maintain operational tempo, leadership must institutionalize a decentralized sustainment paradigm built upon three pillars. The first requires the rigorous enforcement of a Modular Open Systems Approach across all new acquisitions, mandating standardized interfaces to enable rapid, field-level component swapping and mitigate proprietary vendor lock-in.¹⁰ The second pillar demands the operationalization of Fabrication at the Tactical Edge, deploying additive manufacturing capabilities to produce replacement parts on demand, thereby replacing fragile supply chains of physical spares with ruggedized spools of composite filament.¹² The third pillar necessitates the decentralization of operator-maintainer training, transitioning ad-hoc repair skills away from specialized aviation technicians and directly into the hands of the standard infantry maneuver force.¹⁴ By synthesizing lessons from the Ukrainian theater with emerging military pilot programs, this report outlines the critical steps required to build a resilient, self-healing logistical network capable of sustaining drone operations in the modern battlespace.

2. The Strategic Imperative of Autonomous Mass and Contested Logistics

The National Defense Strategy identifies the People’s Republic of China as the primary pacing challenge. In a potential Indo-Pacific conflict, forward air bases and logistical nodes will face sustained, complex attacks from ballistic missiles, cruise missiles, hypersonic weapons, and armed drones.² The capacity and accuracy of adversary long-range strikes have altered combat paradigms, threatening to drive combat aircraft to rear-area bases that are too distant from the operational battlespace to enable combat-relevant operations.⁹ This reality has forced the adoption of dispersed operations through Agile Combat Employment, which drastically complicates the sustainment of combat aircraft and exposes the vulnerabilities of standard, centralized supply chains.⁵

The Logistics of Mass and Attrition

The response to this threat landscape includes the rapid fielding of all-domain, attritable autonomous systems.¹⁸ Unveiled in August 2023, the Replicator initiative aims to field multiple thousands of autonomous systems across multiple domains to counter rapid armed forces buildups.¹ The first iteration of the initiative focuses on fielding these systems by August 2025, while the second tranche, known as Replicator 2, tackles the warfighter priority of countering the threat posed by small unmanned aerial systems to critical installations and force concentrations.¹⁸

Concurrently, the Air Force’s Collaborative Combat Aircraft program is designed to deliver an operational capability before the end of the decade, with plans to produce more than 100 aircraft across the first five years.³ These autonomous platforms will operate alongside crewed fighters, serving as force multipliers that disrupt adversary campaigns and impose crippling costs.¹⁶ The Navy and Marine Corps have similarly launched autonomous wingman programs, reflecting a joint commitment to integrating autonomous systems at scale.²⁰

However, the term “attritable” does not mean entirely disposable after a single use. The strategic value of these systems lies in their ability to maintain a high tempo of operations.²¹ As long as unmanned systems are flying, they impose a cost on the adversary, forcing the expenditure of surface-to-air effectors, interceptors, and electronic warfare resources.²¹ Maintaining this continuous presence requires robust logistics. Combat air forces require personnel, fuel, munitions, ground handling equipment, and replacement materials to generate sorties at scale.¹⁶ The assumption that inexpensive drones can simply be replaced by new units shipped from the continental United States ignores the reality of contested logistics, where adversaries will actively target supply ships, airlift capabilities, and port infrastructure.¹²

The Vulnerability of Class IX Supply Chains

The legacy supply chain for military aviation heavily relies on Class IX supplies, defined as repair parts and components required for the maintenance support of all equipment.⁷ The management of Class IX supplies involves requirements determination, procurement, repair, storage, and long-distance transportation.⁷ In peacetime, readiness-based sparing models calculate the most cost-effective allowances to ensure readiness objectives.⁷ However, wartime usage patterns vary drastically from peacetime forecasting.⁷

In a high-intensity conflict, the demand for specific replacement parts—such as electronic speed controllers, propellers, or specialized sensors—will surge unpredictably.²³ The traditional Logistics Package methodology, which relies on large, off-road capable trucks and trailers to distribute commodities from centralized depots to forward units, presents a massive, slow-moving target.¹⁷ Relying on this outdated system to deliver critical components to dispersed Agile Combat Employment nodes or isolated marine expeditionary units ensures that operational tempo will stall. When supply lines are severed or delayed, units dependent on external resupply for physical spare parts will find their attritable fleets grounded, neutralizing the combat mass these systems were designed to provide.²⁴

3. The Failure of Legacy Depot Maintenance in the Attritable Era

The existing maintenance infrastructure within the Department of Defense is optimized for exquisite, multi-million-dollar platforms. Programs such as the F-35 Joint Strike Fighter or traditional intelligence, surveillance, and reconnaissance aircraft require highly controlled environments, specialized tooling, and extensive diagnostic testing for repairs.²⁵

The Incompatibility of DoDM 4151.23 Frameworks

Traditional organic depot maintenance operations are governed by extensive regulations designed to ensure cost comparability and standard cost accounting. Procedures such as those outlined in DoD Manual 4151.23 require maintenance managers to conduct detailed cost analyses, comparing the cost of organic depot maintenance for similar workloads between different facilities.⁶ This process supports decision-making regarding workload consolidations and make-versus-buy determinations.⁶

Applying this bureaucratic, time-intensive framework to a highly expendable, low-cost drone creates an unworkable logistical bottleneck. Small unmanned aerial systems and the forthcoming Collaborative Combat Aircraft are intended to operate in highly dynamic, time-compressed operational environments where waiting weeks for a cost-benefit analysis or a depot-level repair authorization is tactically fatal.²⁶ When a unit purchases commercial off-the-shelf platforms or fields rapidly acquired systems through the Defense Innovation Unit’s pathways, the traditional requirement to route damaged assets back to stateside depots negates the operational agility of the platform.¹⁹

The Friction of Proprietary Lock-In

Another significant failure point of legacy maintenance models is the reliance on original equipment manufacturers for repairs and upgrades. Historically, defense contractors have utilized proprietary hardware interfaces, encrypted software, and closed architectures to protect intellectual property.¹⁵ In the context of drone warfare, this means that a failure in a specific flight controller or a damaged motor mount might require the entire unit to be returned to the manufacturer, or necessitate the purchase of an expensive, proprietary replacement part that must traverse a vulnerable global supply chain.¹⁴

This dynamic is incompatible with the realities of modern conflict. Combat troops require the flexibility to substitute components from different vendors, adapt payloads to emerging threats, and iterate designs based on immediate battlefield feedback.¹⁵ Treating an attritable drone fleet with the same rigid maintenance protocols as a legacy fighter jet guarantees that the fleet will suffer rapid, unrecoverable attrition, not from enemy action, but from logistical starvation.

4. Operational Realities and Insights from the Ukrainian Theater

The ongoing conflict in Ukraine serves as an unprecedented, real-time laboratory for the integration, employment, and sustainment of autonomous systems at scale. The operational realities observed in this theater invalidate several pre-war assumptions, particularly the notion that drones would deliver decisive effects purely through pristine technological superiority or that they would be rapidly neutralized by traditional air defenses.³⁰ Instead, drone warfare has emerged as a domain characterized by mass, extreme attrition, and continuous adaptation.³⁰

Decentralized Frontline Drone Workshops

To sustain millions of unmanned aerial vehicles on the front lines, Ukrainian forces have abandoned centralized sustainment models in favor of decentralized, highly agile maintenance networks. The operational effectiveness of top Ukrainian drone units is deeply linked to the efficient maintenance functionality of frontline engineering workshops and electronic laboratories.³¹ These facilities are integrated directly within the organizational structure of unmanned aerial vehicle battalions operating under combat brigades, providing emergency repair and modernization in hours rather than days or weeks.³¹

The success of these workshops relies on several critical structural adaptations. First, the workshops are staffed by specialized personnel, typically teams of ten to twelve soldiers who possess engineering or technical backgrounds.³¹ By handling diagnostics, repairs, and the integration of new components, these engineering teams eliminate the technical burden on the drone operators, allowing the pilots to focus entirely on executing daily flight missions.³¹

Second, to counter vulnerabilities from artillery and missile attacks, these technical teams frequently operate from highly mobile repair units.³¹ High-mobility vehicles are equipped with workstations, routers, welding equipment, assembly areas, and soldering stations.³¹ These mobile platforms can operate independently of external power grids for extended periods, ensuring that maintenance operations continue even in austere, heavily targeted environments.³¹

Rapid Adaptation and the Software Lifeline

The integration of engineering teams directly with frontline operators creates an immediate feedback loop that is vital for survival. In a conflict defined by an intense electromagnetic spectrum struggle, static capabilities rapidly become obsolete. When adversary electronic warfare units deploy new jamming techniques, frontline engineers collaborate with operators to devise in-house solutions.³¹ This allows them to change operating frequencies, implement software updates, adjust flight altitudes, and remove identification features that might transmit location data to the enemy in a matter of hours, bypassing lengthy bureaucratic acquisition processes.³¹

Furthermore, these workshops provide critical expertise in explosive ordnance disposal and munition adaptation.³¹ Engineers routinely adapt existing infantry munitions for drone delivery, developing specialized mechanisms to boost the combat capabilities of commercial off-the-shelf platforms.²⁷ The lesson for advanced militaries is that artificial intelligence and automation are most effective as tools for speeding up analysis and coordination, but resilience lies in hybrid, software-defined architectures that push processing, decision-making, and repair capabilities to the tactical edge.³²

Global Observations and Strategic Implications

The innovations emerging from the Ukrainian battlefield are not going unnoticed by global adversaries. Internal military journals and research emerging from Iranian defense institutions demonstrate a concentrated effort to analyze the war in Ukraine to refine their own battlefield doctrine.³³ Senior commanders have studied how forces adapted to stronger adversaries, noting the immense value of small drones, artificial intelligence, and the use of 3D printing for low-cost manufacturing.³³ Analysts are urging leadership to invest heavily in unmanned systems, adopt more mobile combat units, and address gaps in forward planning.³³

For the United States military, the implication is clear. The diffusion of tactical creativity and the institutionalization of rapid adaptation are strategic imperatives. While procuring large quantities of drones is necessary, the true test lies in logistics: the ability to sustain, supply, and regenerate combat power under fire.⁸ Modern high-intensity conflict dictates that frontline workshops and localized maintenance capabilities are not operational luxuries; they are fundamental combat necessities.³⁴

5. The Modular Open Systems Approach (MOSA) as the Sustainment Foundation

To enable ad-hoc, tactical-edge repairs and rapid capability insertion, unmanned systems must be structurally designed for modularity from their inception. The Department of Defense has recognized this imperative, codifying the Modular Open Systems Approach as a legal requirement for major defense acquisition programs under Title 10 U.S.C. 4401(b) and Section 804 of the National Defense Authorization Act.¹⁰

MOSA constitutes an acquisition and design strategy that utilizes technical architectures conforming to widely supported, consensus-based open standards.¹⁰ It mandates the separation of systems into major functions and elements that are loosely coupled and highly cohesive.¹⁰ A key enabler for this strategy is the adoption of an open business model, which permits sharing risk, maximizing the reuse of assets, and incrementally acquiring warfighting capabilities with enhanced flexibility and competition.³⁵

The Strategic Value of Severable Modules

In traditional, proprietary acquisitions, a failure in a specific subsystem might render an entire platform non-mission capable until original equipment manufacturer support can be secured. Under the MOSA framework, systems employ a modular design that uses defined system interfaces between major components.¹¹ This allows severable major system components to be incrementally added, removed, or replaced throughout the life cycle of the platform.¹¹

For drone fleets, this means that a failure in a flight controller, an electronic speed controller, or a navigation module does not condemn the entire airframe.²³ A combat unit can physically swap the damaged module with a replacement component.¹⁰ Furthermore, this interoperability allows for continuous adaptation. If an adversary develops a countermeasure to a specific electro-optical sensor, forces can remove the outdated payload and integrate a new sensor from a completely different vendor, provided both adhere to the same interface standards.¹⁰

The defense industry relies on several foundational open standards to enforce this interoperability across mechanical, electrical, and software domains.

Component Commonality in Collaborative Combat Aircraft

The principles of the Modular Open Systems Approach extend far beyond small, hand-launched quadcopters; they are an absolute necessity for sustaining the Air Force’s larger Collaborative Combat Aircraft. Research and wargames conducted by the Mitchell Institute for Aerospace Studies indicate that sustaining large-scale operations in a Pacific conflict is only feasible if the logistics footprint of the future fleet is strictly minimized.¹⁶

A primary recommendation for force design is to maximize the commonality of components and munitions across different variants.¹⁶ The first increment of these drones currently comprises test articles from multiple vendors, including General Atomics and Anduril Industries.³ If these distinct platforms require entirely unique logistics trains, proprietary ground handling equipment, and specialized testing software, the logistical burden will collapse under the strain of distributed operations.²¹

Senior leadership has stressed that these aircraft must share fundamental components to ease the logistics burden. This includes sharing refueling equipment, weapons loading equipment, motors, actuators, and tires.²¹ Achieving high levels of commonality significantly reduces the volume of bulk consumables and replacement parts that must be transported to dispersed forward operating sites.¹⁶ While the airframes themselves may differ to provide a mission-tailorable mix of capabilities, the underlying architecture must support interchangeable components and plugins based on open application programming interfaces.¹⁶ Logistics and component commonality cannot be treated as an afterthought; they must be defined as core Key Performance Parameters that inform the acquisition strategy from day one.¹⁶

6. Fabrication at the Tactical Edge and Additive Manufacturing

While the Modular Open Systems Approach provides the architectural foundation necessary for field repairs, additive manufacturing provides the physical capability to execute them. The Department of Defense is undergoing a paradigm shift termed Fabrication at the Tactical Edge, a concept designed to decentralize production by leveraging 3D printing and artificial intelligence to enable manufacturing directly on the battlefield.¹²

This approach allows the joint force to design, produce, and deploy equipment as an integral part of operations, effectively closing the acquisition loop within a 24-hour timeframe.¹² By generating mass locally, U.S. forces become highly unpredictable, complicating adversary targeting and counteracting anti-access/area-denial strategies designed to sever long-range supply lines.²⁷

The Logistical Superiority of Filament Over Physical Spares

The traditional sustainment model requires military logistics networks to forecast, procure, transport, and warehouse thousands of distinct physical spare parts. In contested or disconnected environments, these traditional supply lines are slow, vulnerable, and often unavailable.¹³ Additive manufacturing fundamentally alters this logistical equation.

Instead of stocking vast physical inventories of replacement parts for various models, organizations can maintain a digital catalog of parts that can be printed locally, on demand.¹³ When a specific part breaks, it is fabricated on-site. This approach substitutes the transport of fragile, specific spares with the transport of raw materials—specifically, spools of polymer filament and composite resins.¹²

Raw filament is highly space-efficient, durable during transport, and entirely agnostic. A single spool of material can be transformed into a propeller guard, an aerodynamic fairing, an internal bracket, or a customized payload enclosure as the tactical situation demands.¹³ This capability drastically reduces the logistical burden by printing parts instead of transporting spares, allowing units to repair or replace damaged components without waiting on resupply from the rear.¹³ Furthermore, it allows forces to rapidly iterate designs based on field feedback, modifying systems to better suit current mission profiles without relying on a factory production run.¹³

Advanced Materials and Production Methodologies

The viability of 3D-printed parts has surged due to critical advancements in materials science. Historically, manufacturers balanced strength, weight, and cost by relying on a mix of aluminum, steel, titanium, and standard plastics.¹³ As endurance and payload requirements increased, these materials revealed their limitations.¹³ Today, advanced composite materials and structural designs enable performance characteristics that conventional manufacturing cannot easily deliver.²⁹

The industry utilizes several distinct printing technologies to meet operational requirements. PolyJet is effective for high-fidelity prototyping and multimaterial capabilities, while Stereolithography provides high-resolution, smooth aerodynamic surfaces.⁴⁰ For strong, structural components, high-speed Fused Filament Fabrication is the preferred method.⁴⁰ Advanced materials include carbon-fiber-infused Polylactic Acid, Polyethylene Terephthalate Glycol, and Nylon.⁴¹

Carbon-fiber-reinforced composites represent the pinnacle of aerospace-grade additive manufacturing. These filaments merge polymer matrices with carbon fibers to create components with exceptional mechanical properties.²⁹ Carbon-fiber-reinforced components can demonstrate up to a 1243% improvement in Young’s modulus and a 1344% increase in tensile strength compared to standard materials.²⁹ In many applications, these continuous fiber-reinforced composites match or exceed the strength of aluminum at a fraction of the weight, enabling longer flight times and greater payload capacity without sacrificing the durability required for flight.¹³

Mobile Fabrication Nodes and Expeditionary Deployment

To deploy this capability effectively, the military is investing heavily in mobile fabrication nodes designed to withstand harsh field conditions.¹² The Marine Corps has established the Expeditionary Fabrication system, housing polymer and metal printers, alongside milling and grinding tools, inside a standard 8-by-8-by-20-foot container.¹² The Army is pursuing similar capabilities through its Rapid Fabrication via Additive Manufacturing program and has established the Additive Makerspace at Picatinny Arsenal, which houses over 50 advanced printers to drive rapid prototyping.¹²

The versatility of these systems extends to active combat platforms. The Indiana Army National Guard recently achieved a technological milestone by successfully demonstrating 3D printing aboard a UH-60 Black Hawk helicopter mid-flight.⁴⁶ Utilizing a printer designed to withstand air turbulence and physical flight stresses, powered by a portable tactical energy source, the system produced components for unmanned aerial systems while performing tactical maneuvers.⁴⁶ The ability to fabricate precise components on demand directly translates to reduced downtime, boosted readiness, and unmatched flexibility, ensuring that troops can adapt to shifting needs without waiting for external supply chains to catch up.⁴⁶

7. Decentralized Maintenance and Retraining the Maneuver Force

The integration of attritable assets into the tactical edge requires a fundamental paradigm shift in how the military conceptualizes both the operator and the maintainer. Currently, drone maintenance is heavily concentrated within specific Military Occupational Specialties, such as the Army’s 15E (Unmanned Aircraft Systems Repairer) and 15X (Tactical Unmanned Aircraft System Specialist).⁴⁷ These roles require extensive, specialized instruction, encompassing up to 24 weeks of Advanced Individual Training focused on electrical theory, advanced troubleshooting, and payload integration.⁴⁷

While highly specialized technicians remain absolutely essential for maintaining larger, complex Group 3 and Group 4 systems, the stated objective to proliferate small unmanned aerial systems down to every infantry squad renders the specialized-maintainer model unsustainable for lower-tier platforms.¹⁴ The sheer volume of platforms dictates that basic operation, system troubleshooting, and ad-hoc repair must become universal infantry skills, integrating into basic training as seamlessly as traditional marksmanship or rifle maintenance.⁵²

The Cultural Shift: The “Right to Repair”

A critical hurdle to this transition is found in military culture and rigid regulatory constraints. Strict airworthiness releases, intellectual property restrictions tightly held by vendors, and inflexible safety protocols have historically prevented frontline soldiers from modifying their own equipment.¹⁵ However, guided by new drone dominance directives, military leadership is beginning to advocate strongly for the “right to repair”.¹⁵

This cultural shift empowers soldiers to fabricate components, splice wiring, replace electronic speed controllers, and modify system firmware directly in the field. By altering the way contracts are written to secure intellectual property rights from vendors, the military ensures that soldiers have the legal and technical authority to make modifications that suit immediate mission demands without waiting for manufacturer intervention.¹⁴ This is increasingly built into training courses, teaching soldiers how to 3D print, design, code, and rebuild their own systems.¹⁵

Rise of the “Drone Sergeant” and Tiered Frameworks

To bridge the gap between complex aviation engineering and basic infantry skills, the Army is developing tiered maintenance frameworks. A central concept is the formalization of the Company small Unmanned Aircraft System Master Trainer, informally known as the “Drone Sergeant”.¹⁴

This role is designed to be MOS-agnostic, meaning it can be filled by an infantryman rather than a specialized aviation technician. Credentialed via an Additional Skill Identifier, the Drone Sergeant serves as the primary trainer for squad-level operators and the focal point for localized maintenance.¹⁴ Responsibilities include managing localized “bench stocks” of high-use components, executing functional test flights, and conducting intermediate repairs such as soldering and component replacement.¹⁴ This decentralized model frees brigade-level aviation elements from micromanaging squad-level assets, allowing subordinate units to run organic training and currency flights autonomously.¹⁴

At the squad level, individual operators are trained to conduct pre-flight and post-flight checks, perform simple part exchanges such as swapping batteries or propellers, execute firmware updates, and manage lithium polymer battery safety.¹⁴ This tiered approach ensures responsiveness at the point of need while maintaining integration with the broader sustainment enterprise.¹⁴

Specialized Curricula and Standardized Training

The Marine Corps is aggressively operationalizing this decentralized training model to support the mandate of equipping all infantry, reconnaissance battalions, and littoral combat teams with attack drones by mid-2026.⁵⁷ The Marine Corps Training and Education Command has launched a comprehensive suite of six standardized, MOS-agnostic pilot courses designed to rapidly certify operators and maintainers.⁵⁷

These courses address the urgent need for standardized training, doctrine, and force-wide capacity building.²⁸ By teaching payload integration, structural chassis inspection, and component troubleshooting to standard combat troops, the military ensures that damage sustained in combat does not result in permanently degraded unit capability.⁵⁹

8. Predictive Logistics and Data-Driven Sustainment Operations

While decentralization, open architectures, and additive manufacturing provide the physical means to sustain attritable fleets at the tactical edge, data architecture provides the necessary operational direction. Managing thousands of autonomous systems requires a fundamental shift from reactive reporting to anticipatory sustainment.

Current logistics models focus heavily on demand forecasting where units report consumption via enterprise systems, which then feed into automated logistics forecasting during 24 to 72-hour planning cycles.¹⁷ However, the Army’s updated Field Manual 4-0 identifies predictive logistics as a doctrinal imperative, demanding that commanders anticipate equipment failures and optimize resupply before shortfalls actually occur.⁶³ The digital architecture supporting this transition is the Next Generation Command and Control system.⁶³

By integrating real-time data, artificial intelligence, and resilient communications, this system creates a common operating picture for logisticians that is timely, accurate, and actionable.⁶³ At the tactical edge, predictive maintenance utilizes connected sensors and flight maintenance logs to identify wear patterns, such as unusual vibrations in motors or impending battery degradation.⁶²

As Edge artificial intelligence matures, these systems will move beyond simply alerting maintainers to potential hardware failures. They will enable autonomous logistics that request specific filament types or automatically pre-position standardized open-architecture components based on real-time consumption rates and anticipated combat intensity.⁶³ This data-driven approach is absolutely critical to ensuring that the distributed nodes of expeditionary fabrication and localized unit bench stocks are adequately supplied, maximizing readiness without overwhelming the fragile “last tactical mile” with unnecessary or obsolete inventory.¹⁷

9. Strategic Recommendations for Command Leadership

The procurement of thousands of attritable autonomous systems represents a hollow force structure investment if those systems cannot be sustained during high-intensity, multi-domain conflict. To ensure operational readiness when traditional supply lines are severed and depots are compromised, leadership must operationalize the following strategic recommendations:

1. Mandate Open Architecture Compliance in all Future UAS Procurement: Acquisition pathways for all rapid fielding initiatives and Collaborative Combat Aircraft increments must strictly enforce open architectures. Vendors utilizing proprietary physical connectors, encrypted battery interfaces, or closed software ecosystems that prevent tactical-edge component swapping must be disqualified from future tranches. System severability and interface standardization must be codified as primary Key Performance Parameters in all capability development documents.
2. Scale and Fund Fabrication Infrastructure at Echelon: The deployment of 3D printing capabilities must transition from experimental pilot programs to standard Table of Organization and Equipment authorizations. Expedited funding should be directed toward fielding ruggedized expeditionary fabrication units down to the battalion level. Logistics planning must pivot away from forecasting individual drone spares toward calculating the required burn rates of engineering-grade composite filaments, treating raw material as a primary Class IX asset.
3. Formalize Decentralized Sustainment and the “Drone Sergeant”: Service branches must codify roles equivalent to the Company small Unmanned Aircraft System Master Trainer. Personnel policy must be updated to formally sever the requirement for aviation-specific occupational specialties to conduct routine maintenance on lower-tier systems. Furthermore, unit supply chains must establish dedicated lines of accounting to procure commercial components and maintain organic bench stocks directly at the company level.
4. Revise Airworthiness and Safety Doctrine: Current regulations prioritize peacetime safety and bureaucratic oversight over wartime adaptability. The Department must issue broad waivers or revise doctrine to establish the definitive “Right to Repair” for combat units. Soldiers and Marines must be legally, doctrinally, and technically empowered to splice wires, fabricate structural airframes, and integrate ad-hoc payloads without triggering lengthy airworthiness reviews that throttle operational tempo.

By aligning acquisition strategies with the harsh realities of contested logistics, standardizing hardware interfaces, and trusting the maneuver force to adapt and repair their own technology, the military can guarantee that its massive investments in autonomous mass translate directly into enduring, resilient battlefield dominance.

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1. Executive Summary

The modernization of the United States military is increasingly defined by a pivot toward unmanned systems, autonomy, and the algorithmic orchestration of forces. As peer competitors accelerate their own investments in asymmetric and autonomous capabilities, the United States has initiated historic funding measures to field attritable, autonomous platforms at scale. However, strategic analysis indicates a critical vulnerability in this modernization trajectory: a systemic fixation on the acquisition of the physical platforms themselves, often at the expense of the digital, regulatory, and cryptographic connective tissue required to operate them within a multi-national coalition.

The Department of Defense (DoD) is actively pursuing an operational doctrine that relies on Combined Joint All-Domain Command and Control (CJADC2) to network sensors and shooters across all domains. Yet, warfare is inherently a coalition endeavor. If United States unmanned aerial systems (UAS) cannot seamlessly share targeting data, intelligence, surveillance, and reconnaissance (ISR) feeds, and command-and-control (C2) directives with allied partner networks, the sheer mass of deployed platforms will yield diminishing tactical returns. Operating an isolated fleet of drones, regardless of their individual technological sophistication, creates dangerous operational blind spots and fundamentally fractures the coalition battlefield.

This report analyzes the systemic hurdles to allied interoperability in unmanned systems operations. It identifies that acquiring physical capabilities frequently ignores profound integration barriers, specifically within cryptographic standards, cross-domain data sharing, and export control regulations like the International Traffic in Arms Regulations (ITAR) and the Missile Technology Control Regime (MTCR). To successfully enable unified warfighter operations, leadership must mandate integration frameworks that prioritize data-centric security architectures, Commercial Solutions for Classified (CSfC) encryption over legacy paradigms, and strict adherence to the Modular Open Systems Approach (MOSA). Failing to address these systemic requirements will result in a fragmented coalition battlefield, negating the strategic advantage of the DoD’s massive investments in drone technology and ceding decision dominance to adversaries capable of faster, multi-national data synthesis.

2. The Strategic Pivot Toward Autonomous Mass and “Commercial-First” Acquisition

The current fiscal and strategic environment reflects an unprecedented prioritization of unmanned systems and counter-unmanned aircraft systems (C-UAS). The budgetary proposals for the coming fiscal cycles illustrate a paradigm shift from exquisite, easily targeted legacy platforms toward distributed, attritable mass.

Recent budgetary requests for fiscal year 2027 seek to allocate more than $70 billion toward military drones and counter-drone weapon systems, representing the largest investment in drone warfare in United States history.¹ Within this framework, approximately $53.6 billion is specifically requested for autonomy, drone platforms, collaborative combat aircraft, and contested logistics, while $21 billion is designated for munitions and counter-drone technologies.¹ This marks an exponential increase from prior years, such as the fiscal year 2026 allocation, which sought $13.4 billion for autonomous systems and $3.1 billion for C-UAS capabilities.¹

Complementing this baseline funding are rapid acquisition initiatives designed to circumvent traditional bureaucratic procurement delays. The Drone Dominance Program, launched by the War Department with a projected $1.1 billion investment across four phases, aims to rapidly field low-cost, weaponized one-way attack drones across combat units.² Announced in February 2026, this program initiates “The Gauntlet” at Fort Benning, wherein military operators directly assess 25 competing vendors in operational conditions.² By prioritizing battlefield-driven evaluation over traditional acquisition models, the Department expects to issue $150 million in prototype delivery orders rapidly, projecting the fielding of hundreds of thousands of combat-ready systems by 2027.²

This operationalizes priorities outlined in Secretary of War Pete Hegseth’s July 2025 memorandum, signaling a pivot toward attritable mass production.² It echoes lessons drawn from the rapid defense technology ecosystem cultivated by Ukraine following the 2022 Russian invasion.³ Ukraine’s wartime transformation demonstrated that legacy military-industrial complexes, riddled with institutional inertia, are insufficient for modern survival.³ Instead, radical decentralization, bottom-up innovation, and the integration of commercial off-the-shelf (COTS) technologies through dedicated, parallel commercial-first budgets are vital.³

Concurrently, the Defense Innovation Unit’s (DIU) Replicator initiative serves as the strategic response to adversarial quantitative advantages, specifically Chinese military mass.⁴ Launched in August 2023, Replicator 1 targets the fielding of multiple thousands of all-domain attritable autonomous (ADA2) systems by August 2025.⁴ Selected systems include AeroVironment’s Switchblade 600, Anduril’s Altius-600 and Ghost-X, alongside software vendors tasked with enabling swarming navigation.⁴ The subsequent phase, Replicator 2, shifts focus to counter-small UAS (C-sUAS) systems for the protection of critical installations and force concentrations.⁴

However, the rapid acquisition of these platforms obscures deeper systemic vulnerabilities. As operationalized through initiatives like Replicator, autonomy solves the challenge of controlling mass numbers of systems simultaneously in anti-access/area-denial (A2/AD) contested environments without relying on vulnerable operator control links.⁷ Yet, the network backbone required to support hundreds of thousands of sensors transmitting data, and the interoperability necessary to fight jointly with allies, remains a critical work in progress.⁸ The logistical considerations for fueling, maintaining, mapping, and networking thousands of unmanned vehicles across distributed theaters like INDOPACOM will severely test the limits of current architectures.⁸ Procuring the hardware is ultimately the simplest phase; integrating these diverse assets into a cohesive, secure, multi-national network represents the actual operational bottleneck.

3. The Baseline of Coalition Interoperability

Interoperability within a multi-national alliance is not merely a technical specification; it is a multi-layered strategic imperative. The expansion of NATO to 32 nations, coupled with the commitment of allied leaders to invest 5% of GDP annually on defense by June 2025, amplifies the need for seamless integration.⁹ The Defence Production Action Plan, agreed upon at the 2023 Vilnius Summit, and the NATO Industrial Capacity Expansion Pledge from the 2024 Washington Summit, underscore the commitment to harmonizing defense procurement.⁹

However, defining and measuring this interoperability remains complex. Andreas Tolk’s model of Coalition Interoperability outlines nine distinct layers required for success: (1) Physical Interoperability, (2) Protocol Interoperability, (3) Data/Object Model Interoperability, (4) Information Interoperability, (5) Knowledge/Awareness, (6) Aligned Procedures, (7) Aligned Operations, (8) Harmonized Strategy/Doctrines, and (9) Political Objectives.¹¹ The technological challenges (layers 1-4) frequently yield to political and cultural hurdles, but without a unified technical foundation, the higher strategic layers cannot function.¹¹

In the context of drone warfare, this means that a US-manufactured UAS must not only physically fly in allied airspace but must utilize compatible cryptographic protocols, share a standardized data object model for targeting, and feed information into an aligned operational picture. The lack of agreed formats for collection, management, and the communication of findings prevents allies from developing common interoperability measurement tools, often forcing ad-hoc procedural agility that increases mission risk.¹⁰ A core obstacle to overcoming these layers lies in the rigid cryptographic standards currently employed by the US military.

4. Cryptographic Standards and the Security-Interoperability Paradox

A foundational hurdle to allied interoperability in unmanned systems is the strict regime of cryptographic standards enforced by the United States. Achieving seamless data sharing with coalition partners presents a paradox: the systems must be secure enough to protect highly classified National Security Systems (NSS) data from advanced persistent cyber threats, yet accessible and agile enough to allow foreign partners to plug into the network at the tactical edge.

4.1 The Logistical Burden of Legacy NSA Type 1 Encryption

Historically, the DoD has relied heavily on National Security Agency (NSA) Type 1 encryption to protect data at rest and data in transit.¹³ Type 1 products are highly restricted, classified hardware devices (often referred to as HAIPE devices) designed to encrypt and decrypt sensitive national security information using Suite A algorithms.¹⁴ While they offer unparalleled security assurance, their integration into multi-national, distributed unmanned operations imposes massive logistical and operational burdens.

The deployment of Type 1 encryption on autonomous platforms or in distributed multi-national teams creates severe friction. These devices require stringent continuous physical control, specialized handling procedures, and extensive user training that cannot be rapidly imparted to coalition partners or integrated into their distinct C2 networks.¹⁵ Furthermore, Type 1 devices are expensive, bespoke, and often subject to rigid export restrictions, inherently limiting their distribution to foreign allies.¹⁷ In a highly dynamic, distributed drone swarming environment—where nodes are explicitly designed to be “attritable” and are therefore likely to be lost, jammed, or captured—embedding classified hardware creates an unacceptable operational risk.¹⁴ If a drone carrying a Type 1 device is compromised, the incident triggers catastrophic security protocols.

4.2 The Shift to Commercial Solutions for Classified (CSfC)

To overcome this paradox, a pivotal shift toward the NSA’s Commercial Solutions for Classified (CSfC) program is required.¹⁴ CSfC allows organizations to protect classified NSS data using layered Commercial Off-the-Shelf (COTS) technologies, moving away from exclusively developed, classified hardware.¹⁹ By mandating at least two independent, approved layers of encryption (such as MACsec and IPsec protocols), CSfC achieves robust security without the administrative paralyzation of Type 1 devices.¹⁷ If one layer is found to be vulnerable, the secondary layer maintains the integrity of the data.²⁰

The adoption of CSfC fundamentally alters the interoperability landscape. It replaces bespoke hardware with software-driven, commercial standards that can be fielded in months rather than years.¹⁹ For example, commercial tactical networking devices—such as those utilizing Wave Relay mobile ad hoc networks (MANET)—have secured NSA CSfC approval, allowing non-ITAR networking solutions to handle classified data.¹³ This enables warfighters to maintain secure access to classified data even when operating alongside foreign partners on host-nation cellular (5G) or commercial satellite (Starlink) infrastructures, which are otherwise highly exposed domains.¹³ The encryption resides on software within handheld MANET devices or drone payloads, vastly reducing the size, weight, power, and cost constraints.¹⁷

For DoD leadership, the imperative is clear: the integration of unmanned platforms into a multi-national network dictates that acquisition programs explicitly favor CSfC architectures over legacy Type 1 mandates. Without this cryptographic agility, allied forces will remain technologically locked out of the United States’ operational picture, forced to operate through slow, manual liaison channels.

5. Architectural Frameworks: CJADC2 and Cross-Domain Data Sharing

The modern battlefield is inherently multi-domain. Unmanned systems no longer operate in siloed environments; maritime surface drone operations interact with aerial ISR feeds, which are supported by space-based surveillance and ground-based tactical units.²¹ Operating across these spaces requires complex, cross-domain interoperability, which presents significant technical challenges regarding latency, data integrity, and cyber resilience.²¹

5.1 The Evolution of CJADC2

The DoD’s strategic approach to orchestrating this complexity is Combined Joint All-Domain Command and Control (CJADC2). Initiated in 2019, CJADC2 is not a single platform but a warfighting concept and a fusion of technologies, policies, tools, and talent designed to connect sensors and shooters across space, air, land, sea, and cyberspace via a unified network.²⁴ The primary operational goal is to move away from the highly inefficient “swivel chair” model of analysis—where human operators must manually receive data from one isolated system, interpret it, and enter it into another—toward a fully integrated, automated data ecosystem that provides decision advantage.²⁶

Recent milestones demonstrate tangible progress. Following a series of Global Information Dominance Experiments (GIDE), the Chief Digital and Artificial Intelligence Office (CDAO) successfully delivered a Minimum Viable Capability (MVC) for CJADC2.²⁶ This iteration combines software applications, data integration, and cross-domain operational concepts that are characterized as low latency and highly reliable.²⁵ Furthermore, initiatives like Project Olympus, led by the Joint Staff J-6, are actively forging digital pathways to implement mission partner environment architectures on live networks, supporting multi-national operations spanning multiple combatant commands.²⁸

However, the Government Accountability Office (GAO) notes that the DoD has historically struggled to build a comprehensive framework to guide CJADC2 investments enterprise-wide.²⁴ In the absence of strict, centralized direction, military departments continue to pursue C2 projects in isolation, risking duplication of effort and the creation of new, incompatible data silos.²⁴

5.2 Transitioning to Data-Centric Security

A primary barrier to realizing CJADC2 with international partners is overly restrictive data classification.²⁴ Historically, security was approached through network-centric models: building high, secure walls around specific, isolated networks (e.g., SIPRNet) and strictly controlling access to the network itself.²⁸ This model fails catastrophically in a coalition environment where partners utilize disparate national networks and custom infrastructures that take weeks or months to specially bridge and configure.²⁹

To achieve multi-national interoperability, the DoD is fundamentally transitioning to data-centric security.²⁸ Rather than relying solely on network boundaries, data-centricity manages access at the individual data object level.²⁸ Intelligence and ISR data are tagged with specific metadata that determines releaseability based on the attributes, nationality, and clearance of the end-user.²⁸ This paradigm allows for agile and targeted access to critical information on an integrated network, effectively enabling greater information sharing by applying more granular, rather than broader, security controls.²⁸

5.3 Cross Domain Solutions (CDS) as the Mission Enabler

Safely moving this data between domains, classification levels, and coalition networks requires robust Cross Domain Solutions (CDS). A CDS is far more than a simple firewall; it is a specialized device or collection of devices that mediate controlled access and the transfer of information across varying security boundaries.³² In the context of unmanned operations, a CDS enforces defined security policies to automatically and meticulously inspect, sanitize, and validate every single transaction.³²

This allows vital information—such as real-time drone video feeds, cursor-on-target data, command and control instructions, and sensor cueing messages—to flow securely between highly classified networks (like a US Navy combat information center) and unclassified or coalition systems.³² Without integrated CDS, the vast streams of data generated by multi-national ISR platforms would remain trapped in isolated enclaves, creating dangerous operational blind spots and delaying the decision-making kill chain in highly contested environments.³³ Tactical CDS variants (TACDS) guarantee the integrity of mission-critical data when transferred between US networks, Five Eyes (FVEY) coalition networks, and broader NATO partners, stimulating opportunities for real-time data convergence in multi-domain operations.³²

6. The Material Solution: SABRE and the Mission Partner Environment

The conceptual shift toward data-centricity and CJADC2 requires a concrete material solution. The DoD is operationalizing this through the Mission Partner Environment (MPE), which acts as the United States’ primary contribution to federated mission networking.²⁹ The MPE is designed to provide a connected operating environment for US forces and coalition partners, allowing them to exchange information seamlessly.²⁹

The core software tool enabling the MPE is the Secret and Below Releasable Environment (SABRE).³⁰ SABRE provides a globally connected, continuous collaboration environment that allows US Combatant Commands, select allies, and interagency partners to share tactical data using their own disparate networks.³¹ Hosted across geographically dispersed, government-owned/contractor-operated classified cloud production environments, SABRE liberates data from incompatible silos.²⁹

SABRE leverages strict data-centric protocols, incorporating standards like ADatP-4774 (Confidentiality Metadata Label Syntax) and ADatP-4778 (Metadata Binding Mechanism) to ensure that information shared within chat environments or C2 applications is accurately tagged for releaseability.³⁷ This ensures that when a US drone identifies a target, the intelligence can be instantly routed through SABRE to a partnered artillery unit whose network is authorized to view that specific classification of data, cutting the decision-making kill chain drastically.³¹

Recent tests, such as the Capstone 2025 event conducted by the US Air Force’s Battle Lab (ShOC-N), demonstrate SABRE’s potential. By integrating joint forces alongside Five Eyes partners (the UK and Canada), the experiment successfully assessed the interoperability of AI and machine learning tools, advancing cross-national technological collaboration in dynamic targeting scenarios.³⁸

7. Navigating the Regulatory Labyrinth: Export Controls and Technology Transfer

While cryptographic architecture and data-centric software provide the technical means for interoperability, the policy frameworks governing the export and transfer of military technology frequently create profound procedural delays. The goal of integrating allied capabilities is consistently undermined by outdated export control systems originally designed to contain Cold War proliferation.

7.1 Reinterpreting the Missile Technology Control Regime (MTCR)

The Missile Technology Control Regime (MTCR) is a voluntary multilateral arrangement established in 1987 to limit the proliferation of delivery systems capable of carrying weapons of mass destruction.³⁹ Under the MTCR Guidelines, “Category I” items are subject to a strong presumption of denial for export.³⁹ Historically, the regime categorized complete unmanned aerial vehicle systems capable of a 300-kilometer range and a 500-kilogram payload directly alongside ballistic missiles and space launch vehicles.³⁹ Furthermore, “range” under these regulations is rigidly defined as the maximum distance an aircraft can travel in one direction under perfect, fuel-efficient flight conditions, completely independent of operational reality, payloads, or telemetry limits.⁴²

This categorization severely hindered the United States’ ability to export capable, long-endurance drones to allied nations. By enforcing an even higher bar than the MTCR Guidelines required, the US unintentionally ceded global drone market share to nations operating outside the regime’s strict interpretations, such as China and Turkey, fracturing the technological baseline of the broader NATO alliance.⁴⁰

Recognizing this strategic failure, the US Department of State implemented a crucial policy shift in September 2025.⁴³ The update explicitly reinterprets MTCR rules, directing that requests to export military UAS will now be reviewed under policies similar to those for crewed fighter aircraft, rather than as missile systems.⁴¹ This fast-track framework reduces bureaucratic friction, streamlines Foreign Military Sales (FMS) approval, and signals a shift toward faster capability adoption by trusted allies.⁴¹ Faster exports open the door for adjacent innovations—secure communications, counter-UAS systems, and swarming software—which are prerequisites for achieving platform interoperability across NATO and the Indo-Pacific.⁴⁴

7.2 ITAR Constraints and the Algorithmic Classification Trap

The International Traffic in Arms Regulations (ITAR) similarly governs the export of defense-related technologies.⁴⁵ Drone technologies intended for military use reliably fall under the US Munitions List (USML) Categories IV, VIII, or XII, depending on their capability.⁴² While exporting the physical airframe presents known regulatory challenges, a unique and severe bottleneck emerges regarding artificial intelligence (AI) and autonomous software.

As the DoD aggressively pursues collaborative combat aircraft and autonomous swarming logic to fulfill Replicator’s mandate, the algorithms driving these systems are heavily scrutinized. If an AI layer provides capabilities such as target identification, weapons guidance, electronic-warfare countermeasure optimization, or autonomous strike authorization, the Directorate of Defense Trade Controls (DDTC) views the algorithm as fundamentally inseparable from the defense article itself.⁴⁶ Thus, the software—even if derived from commercial origins—becomes ITAR-controlled technical data.

Furnishing assistance to foreign persons in designing or optimizing these targeting algorithms triggers severe defense-service exposure.⁴⁶ This creates an intractable bottleneck in multi-national drone operations. If an allied nation purchases an American drone platform but cannot access, update, or integrate its underlying autonomy software with their indigenous C2 systems due to ITAR technical data constraints, the platform remains functionally isolated. Prototyping and program calendars shrink significantly when engineers must treat ITAR as a design constraint rather than a post-design hurdle, requiring secure data flows and centralized U.S. fabrication footprints.⁴⁷

7.3 The AUKUS Exemption Framework

To mitigate these regulatory choke points among the closest allies, the US implementation of the ITAR § 126.7 Exemption for the AUKUS partnership (Australia, the United Kingdom, and the United States) represents a landmark legislative shift.⁴⁸ Effective September 1, 2025, this exemption grants the UK and Australia a privileged status comparable to Canada, officially recognizing their export control systems as comparable to those of the United States.⁵⁰

This significantly reduces the requirement to obtain individual DDTC licenses for defense trade among Authorized Users.⁴⁸ By removing restrictions on transfers, the exemption theoretically allows for deep defense industrial integration and co-development of advanced UAS capabilities.⁵⁰ However, aerospace industry analysis warns that the operational success of this exemption depends entirely on its administrative implementation.⁵¹ Current treaty exemptions are often ignored by industry due to disproportionate administrative burdens; if the scope of Congress’s intent is unnecessarily narrowed by legacy bureaucratic reflexes, the friction will persist.⁵¹ Expanding such streamlined, reciprocal regulatory models beyond AUKUS to the broader NATO alliance is critical to meet the operational timelines demanded by the Drone Dominance Program.

8. Tactical Integration Frameworks: Bridging the “Day Zero” Gap

The strategic alignment of cryptographic policies and export control reform must eventually translate into tangible, tactical capability at the edge. “Day Zero” interoperability refers to the capability of multi-national systems to function seamlessly in a coalition operation from the very first moment of deployment, without requiring extensive in-theater retrofitting.⁵³ Achieving this requires robust, pre-established integration frameworks.

8.1 Federated Mission Networking (FMN)

NATO’s broad approach to Day Zero interoperability is rooted in Federated Mission Networking (FMN).⁵⁴ Evolving from the Afghanistan Mission Network (AMN)—which was initially built to unify disparate national communication systems into a single operational picture within a specific theater—FMN establishes standardized operating procedures and capability baselines for future coalition warfare.⁵⁴

FMN dictates that participating nations confirm their communication systems comply with NATO security and interoperability principles prior to allocation.⁵⁶ It relies heavily on standardization across modelling and simulation, utilizing protocols like the High Level Architecture (HLA) and Command and Control Systems – Simulation Systems Interoperation (C2SIM).⁵³ However, the adoption of FMN faces severe technical, logistical, and bureaucratic challenges.⁵⁷ The slow pace of allied defense procurement often prevents the rapid adoption of innovative technology required to meet FMN baselines.⁵⁷ Furthermore, the continuous need for rigorous Federated Service Management and Control (FSMC) to align IT components across borders means that FMN is often too cumbersome for rapid, tactical deployments involving newly acquired commercial drone tech.⁵⁸

8.2 The Mission Partner Kit (MPK) Paradigm

While strategic networks like FMN mature at the enterprise level, tactical units require immediate solutions. The development of the Mission Partner Kit (MPK) by the US Army’s 2nd Cavalry Regiment (2CR) provides a highly effective blueprint for rapid, tactical-level interoperability.⁵⁹ Informed directly by the rapid assimilation of commercial technologies in the Ukraine conflict, the MPK addresses the severe delays and operational risks caused by the fundamental incompatibility of partner radio networks and C2 systems.⁵⁹

The MPK is entirely software-centric and platform-agnostic, leveraging COTS applications built upon the Army’s Nett Warrior foundation.⁵⁹ Hosted in government-approved commercial clouds and secured via zero-trust cybersecurity principles and software-based encryption, it completely circumvents the need for specialized, ITAR-restricted hardware encryption devices.⁵⁹

By allowing multinational partners to access applications via simple quick response (QR) codes on any mobile device—or by allowing US units to issue pre-loaded commercial smartphones to allies lacking compatible tech—the MPK instantly brings allied forces onto a synchronized Common Operating Picture (COP).⁵⁹ Tested successfully during major NATO exercises like Griffin Shock 23 and Saber Strike 24, the MPK allowed German battalion leaders to report operational data and checkpoint crossings to US headquarters in real-time, completely bypassing traditional technical and cryptographic barriers that historically plagued joint exercises.⁵⁹

9. Platform Standardization: STANAG 4586 and the Modular Open Systems Approach (MOSA)

To ensure that the physical drone platforms acquired by the DoD can integrate into these tactical and strategic networks, strict adherence to engineering standards is required. The aerospace market’s gradual transition from closed, proprietary, monolithic systems to open architectures is vital for multi-domain operations.⁶¹

9.1 NATO STANAG 4586

For unmanned aerial vehicles specifically, interoperability relies heavily on standardization agreements. STANAG 4586 establishes the standard interfaces for UAV Control Systems (UCS) within NATO.⁶¹ It defines critical architectural elements, specifically the Vehicle Specific Module (VSM) and the Data Link Interface (DLI).⁶² The DLI provides a common set of messages and formats to enable communication between a variety of air vehicles and compliant control systems.⁶⁴ Ensuring compliance with STANAG 4586 means that a control station operated by one NATO nation can effectively communicate with, task, and operate a drone manufactured by another, breaking down the siloed procurement models of the past.⁶¹

9.2 The Modular Open Systems Approach (MOSA)

This philosophy of open architecture is broadly encapsulated in the DoD’s mandate for a Modular Open Systems Approach (MOSA).⁶⁵ Mandated by Title 10 U.S.C. 4401(b) for all major defense acquisition programs, MOSA dictates that systems be designed using modular interfaces between major components.⁶⁶ By adhering to widely supported, consensus-based standards—such as Open Mission Systems (OMS) for aviation, the Future Airborne Capability Environment (FACE) for software, and the C5ISR/EW Modular Open Suite of Standards (CMOSS)—MOSA ensures that sensors, communications payloads, and flight control software are severable.⁶⁶

Embracing MOSA prevents vendor lock-in, reduces total lifecycle costs, and facilitates continuous technology refresh.⁶⁵ Crucially for interoperability, a MOSA-compliant drone allows the Department of Defense to strip out a proprietary US communications module and replace it with an allied nation’s sovereign radio system, or upgrade a targeting sensor without requiring a total system redesign.⁶⁸ The operational flexibility to configure available assets to meet rapidly changing, multi-national operational requirements is a direct result of strict MOSA enforcement.⁶⁸

10. Strategic Directives for Department of Defense Leadership

The tendency of American defense planning to fixate on the technological specifications of the platform itself—speed, payload capacity, and autonomous swarming capability—ignores the stark reality that a drone is only as lethal and effective as the network it inhabits. The Department of Defense cannot achieve unified warfighter operations within a multi-national coalition through raw procurement scale alone; it requires deliberate, mandated integration frameworks established and enforced from the highest levels of leadership.

The planned expenditure of over $70 billion on drone warfare, alongside rapid procurement mechanisms like the Drone Dominance Program and Replicator, represents a decisive commitment to modernizing the force.¹ However, as the logistical and procedural friction of previous INDOPACOM experiments demonstrates, pushing programs rapidly through the acquisition system is vastly easier than employing them effectively alongside allies in combat.⁸

To overcome the systemic hurdles of cryptographic standards, cross-domain data sharing, and export control friction, DoD leadership must aggressively pursue the following strategic directives:

1. Mandate Data-Centric Security Across All Acquisitions: The era of network-centric “high castle wall” security is incompatible with coalition warfare. Leadership must ensure that all newly acquired unmanned systems and C2 software integrate natively with data-centric models like SABRE and the Mission Partner Environment (MPE).²⁸ The ability to apply zero-trust authentication and tag individual ISR data objects for releaseability must be written into the base requirements of the Replicator initiative.⁴ Without this, the United States will field hundreds of thousands of drones that are technically incapable of sharing sensor data with the allies fighting alongside them.

2. Default to Commercial Solutions for Classified (CSfC): To ensure allied forces can plug into the tactical network, leadership must shift away from the reflexive use of highly restrictive, bespoke Type 1 encryption hardware for attritable edge devices.¹⁴ Embracing CSfC frameworks that utilize dual-layered software encryption (such as MACsec and IPsec) allows for the deployment of advanced MANET networks on commercial hardware.¹³ This provides the agility required to securely connect disparate forces in contested environments without the untenable logistical burdens and export restrictions of legacy classified equipment.¹⁴

3. Standardize Joint Interoperability Frameworks: Agreements such as the US/UK Joint Declaration of Intent to establish common data standards for C-UAS technologies, driven by Joint Interagency Task Force 401 (JIATF-401), must become the default operational standard rather than exceptional milestones.⁷⁰ The integration of diverse capabilities is heavily hindered by incompatible data formats; by mandating that vendors comply with joint data standards to participate in defense marketplaces, the DoD forces the industry to build interoperability natively.⁷⁰ Furthermore, compliance with NATO STANAG 4586 for control system interfaces must be rigorously enforced to prevent closed, proprietary ecosystems from fracturing the alliance.⁶¹

4. Fully Operationalize MOSA Requirements: The Modular Open Systems Approach must transition from a theoretical preference to an absolute contractual necessity in all drone procurements.⁶⁶ Leadership must utilize legal frameworks and acquisition strategies to ensure that all drone platforms utilize severable, modular architectures with open interfaces like FACE and CMOSS.⁶⁵ This allows for the rapid integration of third-party software—including allied targeting algorithms or sovereign communications protocols—without requiring total system redesigns by the original manufacturer.⁶⁸

5. Aggressively Pursue Export Control Modernization: While the recent MTCR reinterpretation and the AUKUS ITAR exemptions are vital steps forward, they must be rigorously implemented and expanded.⁴⁰ Leadership must work closely with the Directorate of Defense Trade Controls (DDTC) to establish clearer guidelines for the export of AI-driven autonomy software.⁴⁶ If the algorithmic layers of drones remain trapped behind impenetrable ITAR defense-service firewalls, true collaborative swarming and co-development with coalition partners will remain impossible.⁴⁶

The defining metric of success for the United States’ unmanned systems strategy will not be the raw number of attritable platforms fielded by 2027. Rather, success will be measured by the speed, security, and interoperability of the network connecting them. It will be defined by the latency with which a US autonomous sensor can detect a threat, process the targeting data through a Cross Domain Solution, navigate ITAR releaseability protocols via SABRE, and securely transmit firing coordinates to an allied strike asset. By pivoting strategic focus from the platform to the network—enforcing data-centric architectures, prioritizing CSfC and MOSA frameworks, and dismantling antiquated export constraints—the Department of Defense can ensure that its massive investments yield a truly unified, interoperable, and overwhelmingly lethal coalition force.

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1. Executive Summary

The United States Department of Defense (DoD) is in the midst of a foundational paradigm shift regarding the procurement, deployment, and operational integration of unmanned aerial systems (UAS). Driven by strategic initiatives such as Replicator, the DoD aims to field all-domain attritable autonomous systems at an unprecedented scale to offset the quantitative and anti-access/area-denial (A2/AD) advantages of near-peer competitors.¹ This initiative represents a recognition that the character of warfare has fundamentally changed; massed, low-cost precision strike capabilities are replacing solitary, exquisite platforms as the primary arbiters of tactical success.³ However, while institutional focus and capital investment are overwhelmingly directed toward the kinematic capabilities and production scale of these airframes, a critical operational vulnerability remains largely unaddressed: the systemic enterprise architecture required to conduct accurate, near-real-time post-strike Battle Damage Assessment (BDA).

When hundreds of autonomous assets engage a target matrix simultaneously, the resulting battlespace becomes highly opaque. The traditional methodology for evaluating strike effectiveness relies heavily on centralized Intelligence, Surveillance, and Reconnaissance (ISR) assets, supported by human-in-the-loop Processing, Exploitation, and Dissemination (PED) workflows.⁴ These legacy systems, developed for deliberate operations, are entirely unsuited for the speed, volume, and complexity of autonomous swarm engagements. The inability to rapidly verify target destruction, attribute specific kinetic effects to individual platforms amidst heavy electronic warfare, and dynamically update the Common Operating Picture (COP) creates severe situational awareness deficits for commanders operating at echelon.⁵

This strategic report identifies the doctrinal, technological, and enterprise-level methodology gaps that DoD leadership must address to ensure swarm technologies yield decisive operational advantages. The analysis evaluates the physical challenges of massed detonations, the necessity of multi-modal sensor fusion in degraded environments, the imperative for edge-based neuromorphic processing, and the legal and ethical requirements for establishing accountability in AI-driven targeting.⁸ Failure to modernize the BDA enterprise concurrently with UAS procurement risks fielding a force capable of mass destruction but incapable of operational assessment, leading to inefficient resource allocation, disrupted mission command, and significant strategic liability. Addressing these methodology gaps is not a secondary sustainment concern; it is a primary warfighting requisite for the future joint force.

2. Strategic Context: The Proliferation of Autonomous Mass

The character of contemporary warfare is undergoing a rapid evolution characterized by the democratization of precision fires. For decades, the United States maintained a near-monopoly on precision strike capabilities, relying on deep magazines of advanced munitions delivered by highly survivable, yet incredibly expensive, platforms.³ The proliferation of cheap drone technology has fundamentally altered this landscape, rendering traditional models of air dominance and force protection increasingly vulnerable.³

2.1 The Replicator Initiative and the Offset Strategy

The DoD’s introduction of the Replicator initiative signifies a concerted effort to allocate resources toward the fielding and deployment of all-domain expendable autonomous capabilities at a scale capable of yielding significant operational impact.¹¹ The core objective of this initiative is to thwart the asymmetric advantages of adversaries—particularly the People’s Liberation Army (PLA) of China—through the application of many, small, “attritable” weapons and combat platforms.² The PLA is rapidly advancing its drone capabilities by developing more autonomous systems and acquiring them at scale.³ Without deep magazines of autonomous capabilities and the supporting architectures to manage them, the United States risks having its distributed warfighting strategies overwhelmed by massed drone attacks.³

Replicator poses an opportunity for the U.S. Army and the broader Joint Force to continuously transform concepts, capabilities, and capacities.² However, as the DoD integrates lessons learned from executing the first iterations of the Replicator initiative, leadership must recognize that scale alone is an insufficient countermeasure.¹¹ A pronounced production advantage must be paired with a concerted innovation effort focused on optimizing tactical efficacy.¹¹ Efficacy is directly tied to the ability to assess, adapt, and redirect force—all of which rely entirely on the BDA enterprise.

2.2 Operational Lessons from Contemporary Conflicts

Observations from the Russo-Ukrainian war and conflicts in the Middle East provide a stark preview of the future battlefield. The front lines have expanded into wide “kill zones” where drones detect and strike targets across vast areas with unprecedented precision.¹³ Ukrainian commanders have leveraged the relatively low cost and high accuracy of these systems to develop new tactical concepts, employing first-person-view (FPV) drones for real-time reconnaissance and loitering munitions for precision strikes against enemy armor, artillery, and command posts.¹⁵

The integration of UAS with artillery has been particularly transformative, enabling real-time adjustments of fire and immediate battle damage assessment, thereby changing the entire calculus of fire support.¹⁵ However, this operational success is currently predicated on heavy human-in-the-loop involvement. Warfighters manually pilot FPVs, manually assess the video feeds, and manually call for adjustments. As conflicts scale and electronic warfare environments become more hostile, this manual methodology becomes unsustainable. The Ukrainian military’s stated objective is to eventually remove warfighters from direct combat and replace them with autonomous unmanned systems, recognizing that human capacity to process and fuse large amounts of data is a critical vulnerability.¹⁶

Furthermore, operations in Syria demonstrate the evolving use of massed systems. During Operation Spring Shield in 2020, Turkish forces grouped armed UAVs together in significant numbers—described as “swarms”—with the specific aim of overwhelming opponent air defenses.¹⁷ This approach negated the need to ensure that ground-based air defenses were fully neutralized prior to engagement, as the drones themselves acted as both the sensor and the kinetic effector.¹⁷ As these tactics evolve from remote-controlled operations to fully autonomous algorithmic swarms, the necessity for an automated, enterprise-level BDA capability becomes paramount.

3. The Doctrinal Chasm: Legacy BDA Frameworks Versus Swarm Velocity

Current DoD joint targeting doctrine is primarily codified within publications such as Joint Publication 3-60 (JP 3-60). This doctrinal framework was meticulously developed for an era of deliberate, single-platform precision strikes and relies upon methodologies that represent a fundamental mismatch with the operational realities of autonomous drone swarms.¹⁸

3.1 The Structural Limitations of Joint Publication 3-60

Targeting encompasses many processes, all linked and logically guided by the joint targeting cycle, which continuously seeks to analyze, identify, develop, validate, assess, and prioritize targets for engagement.¹⁹ Combat assessment measures whether desired effects are created, if objectives are achieved, and what next steps are required.⁴ According to established doctrine, the BDA process is divided into distinct, chronological phases.

Phase I BDA focuses on initial functional damage assessment. This initial reporting is generally expected within a 24-hour window after the information becomes available.⁴ Phase II assesses specific target element damage. Phase III, known as Target System Assessment, evaluates the broader impact on an adversary’s overall capabilities. Doctrine explicitly describes Phase III as a “data-intensive process” that “typically requires weeks to months to accumulate the data to assess the impact on the target system”.⁴

In the context of a massed drone strike, where hundreds of loitering munitions or small FPV drones may engage an enemy defensive line within a span of minutes, a 24-hour feedback loop is tactically obsolete. Autonomous swarm logic relies on instantaneous, continuous feedback to effectively reallocate surviving airborne assets to undestroyed targets.²⁰ If a swarm must hold position or return to base to wait for external ISR platforms to conduct a Phase I assessment, the principles of mass, momentum, and operational tempo are entirely forfeited.

Furthermore, JP 3-60 explicitly acknowledges a critical methodology gap: the limited availability of collection assets. The doctrine states that Intelligence, Surveillance, and Reconnaissance (ISR) and Processing, Exploitation, and Dissemination (PED) assets are “usually limited in number”.⁴ In operational reality, collection requirements for target development, Joint Intelligence Preparation of the Operational Environment (JIPOE), and indications and warnings frequently take precedence over combat assessment.⁴ Relying on these scarce, highly centralized assets to monitor and evaluate hundreds of simultaneous drone strikes is mathematically and operationally untenable.

3.2 The Operational Risk of Estimated Damage Assessment (EDA)

In scenarios where physical confirmation of damage is unavailable—a highly probable situation in heavily contested, A2/AD airspace where dedicated BDA ISR assets cannot survive—doctrine permits the use of Estimated Damage Assessment (EDA).⁴ The EDA methodology anticipates damage by utilizing probabilistic models based on the known effectiveness of specific weapons against specific target types. This allows a commander to accept operational risk in the absence of definitive visual data.⁴

Relying on EDA methodologies for massed drone strikes introduces profound strategic and tactical risk. Unmanned systems, particularly the lower-cost “attritable” models envisioned by the Replicator initiative, possess highly variable failure rates, payload yields, and navigation vulnerabilities compared to traditional munitions.²¹ If a swarm of 500 autonomous drones is launched against a mechanized brigade, and the EDA methodology assumes an 85% success rate based on pre-flight probabilities, operational commanders may erroneously advance friendly maneuver forces into fully intact enemy defensive networks. Alternatively, if commanders lack confidence in the EDA due to known high attrition rates of small UAS, they may authorize continuous re-attacks on already destroyed targets, rapidly depleting the finite magazine depth of the swarm and stressing logistical supply chains.²⁰

3.3 Munitions Effectiveness Assessment (MEA) Latency

Another significant doctrinal gap exists within the Munitions Effectiveness Assessment (MEA) framework. MEA evaluates whether a weapon functioned as engineered and intended.⁴ Currently, MEA data generation relies on a long-term feedback loop. The intelligence gathered is typically funneled into the Joint Munitions Effectiveness Manual (JMEM) revision process to inform future capability analysis, rather than providing an immediate tactical adjustment for ongoing engagements.⁴

For drone swarms to function as intelligent, adaptive combat systems, MEA must transition from a retrospective analytical tool to a near-real-time tactical capability. If an adversary introduces a novel electronic warfare (EW) jamming technique, a new directed energy weapon, or a physical countermeasure that causes a specific munition to fail in the terminal phase, the swarm must immediately recognize this failure.²³ It must then rapidly shift tactics, alter approach trajectories, or switch sensor modalities. A delayed MEA feedback loop renders the entire massed swarm highly susceptible to a single, rapidly deployed countermeasure, potentially neutralizing the entire force package before human analysts even register the failure.²²

4. The Physical and Environmental Realities of Massed Strikes

The visual and electromagnetic environment resulting from a massed drone strike creates immense physical barriers to accurate post-strike assessment. The sheer density of kinetic events generates systemic interference that routinely blinds traditional optical sensor arrays, necessitating a complete overhaul of how autonomous systems perceive the post-strike battlespace.

4.1 Visual Occlusion, Thermal Blooming, and Electromagnetic Chaos

When kinetic energy weapons, such as the shaped charges or fragmentation payloads carried by loitering munitions, impact their targets, they deposit massive amounts of kinetic and thermal energy, generating highly localized destruction.²⁴ In a coordinated mass strike involving dozens or hundreds of detonations within a tightly confined geographical area, the resulting physical phenomena actively obscure the battlefield from observation.

The primary impediment is particulate obscuration. Pulverized concrete, displaced earth, fragmented armor, and combustion smoke create a dense, persistent aerosol layer over the target area. Traditional visual (RGB) cameras, which are heavily relied upon for FPV targeting and basic intelligence gathering, cannot penetrate this layer.¹⁰

Simultaneously, the heat generated by consecutive explosions saturates infrared (IR) sensors, a phenomenon known as thermal blooming. An incoming follow-on drone attempting to assess the damage of a preceding strike wave will find its thermal optics blinded by the residual heat signature of the destroyed target, the burning terrain, and the atmospheric distortion.¹⁰ This makes it nearly impossible for basic algorithms to differentiate between a burning, destroyed vehicle and the still-intact armor positioned adjacent to it.

Furthermore, these massed detonations, coupled with active adversary electronic warfare and the necessary friendly jamming meant to protect the swarm from counter-UAS systems, create a highly contested and chaotic electromagnetic spectrum (EMS).²¹ The denial of the EMS affects friendly units just as severely as adversaries.²¹ If a swarm is programmed to strike in rapid succession, the drones arriving at the target area moments after the initial wave are flying into an environment that is visually, thermally, and electromagnetically opaque. Without specialized, multi-modal methodologies to see through this post-strike fog, follow-on drones cannot conduct BDA, nor can they accurately acquire secondary targets.

4.2 The Imperative of Multi-Modal Sensor Fusion

To overcome these severe physical limitations, the enterprise BDA methodology must definitively shift from single-sensor reliance to automated, multi-modal sensor fusion driven by advanced neural networks.¹⁰

Recent research in remote sensing and disaster monitoring demonstrates that relying solely on Electro-Optical (EO) or IR sensors is entirely insufficient for highly obscured environments.¹⁰ Advanced methodologies necessitate the integration of Synthetic Aperture Radar (SAR) with high-resolution UAV-based optical and thermal imagery.¹⁰ SAR possesses the unique physical capability to penetrate dense smoke, heavy cloud cover, and airborne obscurants, providing high-fidelity topological mapping and structural analysis of the target area regardless of visual conditions.¹⁰

Implementing this level of multi-modal fusion across a swarm requires highly sophisticated neural network architectures capable of operating on constrained hardware. For example, hybrid learning frameworks utilizing Vision Transformers (such as FPANet) can capture both local textures and global spatial dependencies to achieve robust segmentation from SAR data under cloudy or smoky conditions.¹⁰ Simultaneously, models designed specifically for the synergistic fusion of thermal and RGB imagery (such as DualSegFormer) ensure high-fidelity target delineation even when visibility is partially compromised.¹⁰ Other optimization algorithms, such as customized versions of YOLOv8 utilizing High Intersection over Union (HIoU) loss functions, dynamically adjust the weight of various visual components to achieve precise target localization despite background noise.²⁷

The core enterprise challenge for the DoD is not merely acquiring these diverse sensors, but engineering the algorithms that allow attritable, low-cost drones to fuse this disparate data organically and autonomously.

5. Enterprise Architecture and Telemetry Bottlenecks

The defining characteristic of a functional drone swarm is its interconnectedness—the ability of multiple independent agents to share data and act cooperatively.²⁰ However, this critical connectivity creates a massive structural vulnerability when applied to traditional BDA methodologies, which rely on moving large packets of raw data back to human analysts.

5.1 The Bandwidth Paradox and Electromagnetic Contestation

A swarm comprising hundreds of drones, each equipped with visual, thermal, SAR, and telemetry sensors, generates an astronomical volume of data continuously.⁹ In a peacetime, uncontested environment—such as a disaster response scenario or a domestic training exercise—streaming high-definition multi-modal data from multiple platforms to a centralized Ground Control Station (GCS) is feasible via 5G networks and unhindered line-of-sight communications.²⁹ In a large-scale combat operation (LSCO) against a near-peer adversary, this data architecture will immediately collapse.

Adversaries will employ aggressive electronic warfare (EW), attempting to jam the radio-frequency links required for both command and control (C2) and data transmission.²³ Furthermore, wide-area and wide-spectrum jamming operations inherently affect both friendly and enemy units. To operate effectively, friendly forces must meticulously map, interpret, and deconflict their own EMS usage to avoid electronic fratricide.²¹ Consequently, the available bandwidth for a swarm operating over a contested target area will be severely constrained, highly intermittent, or entirely denied for extended periods.

5.2 Edge Computing and Semantic Compression Methodologies

To successfully execute BDA under these highly contested conditions, the fundamental methodology of data processing must be inverted. Instead of transmitting raw, high-bandwidth data (such as live video feeds or raw radar returns) back to human analysts for processing, the data must be analyzed autonomously on the drone itself, and only the resulting assessment transmitted. This architectural shift relies on edge computing and semantic compression.³¹

Onboard edge processing capabilities drastically reduce latency by analyzing data locally rather than transmitting it to remote servers.³¹ From a practical standpoint, instead of attempting to transmit a gigabyte of video showing a burning enemy surface-to-air missile system, the drone’s onboard AI processes the video, confirms the destruction of the target against its pre-loaded threat library, and transmits a kilobyte-sized text telemetry packet: “.

This methodological shift is critical for the viability of massed autonomous operations. It transforms the swarm from a collection of “dumb” aerial cameras requiring massive, vulnerable data pipelines into a decentralized network of distributed intelligence nodes requiring minimal bandwidth to rapidly update the COP.

5.3 Neuromorphic Computing for Advanced RF Analysis

Achieving this level of sophisticated edge computing on small, attritable platforms presents a significant hardware challenge. Traditional processors consume substantial power and generate heat, which directly reduces the flight time, range, and payload capacity of small UAS.⁶ To bridge this gap, the DoD is currently funding research into advanced methodologies, particularly the application of artificial intelligence based on neuromorphic networks.⁹

Neuromorphic computing seeks to replicate human brain functionality at the nanoscale using man-made artificial neurons and synapses.⁹ This architecture allows for highly parallelized computing, with vast amounts of memory located in immediate proximity to the computing elements. The result is substantially increased processing speed coupled with drastically reduced power consumption.⁹ For military applications, a critical advantage of neuromorphic networks is their ability to operate in GHz and even THz frequency ranges.⁹ This high-frequency property allows the neural network to process microwave and RF signals directly at the carrier frequency without the power-intensive need for prior digitization or super-heterodyning.⁹

A swarm equipped with low-power neuromorphic processors could instantly analyze the complex RF signatures of a contested environment, detect the emissions of an enemy radar system, assess the functional damage of that electronic target post-strike by noting the cessation or alteration of its signal, and share that assessment across the swarm instantly, all while operating under stringent power and bandwidth limitations.

6. Methodological Paradigms for Attributing Kinetic Effects

In a legacy dispersed targeting scenario utilizing single platforms, attributing a kinetic effect is highly straightforward: one weapon is deployed against one target, and the resultant outcome is assessed directly.²¹ However, in a mass precision strike, where salvos of hundreds of effectors are launched to overwhelm point defenses at key sites, attributing kinetic effects becomes a mathematically and tactically complex “many-to-many” problem.²¹

6.1 The Challenge of Distinguishing Intercepts from Impacts

When an autonomous swarm of 200 drones assaults a heavily defended position, the adversary’s air defense artillery (ADA), electronic warfare elements, directed energy weapons, and kinetic counter-UAS systems will engage the swarm simultaneously.³ If 60 drones detonate mid-air due to kinetic intercepts, 40 crash indiscriminately due to intense EW jamming, and 100 successfully strike their designated targets, the resulting battlespace telemetry is highly ambiguous.

A critical BDA methodology gap is the enterprise’s ability to distinguish a mid-air intercept from a successful target impact based solely on the loss of platform telemetry. Currently, if an attritable drone loses connection or its telemetry suddenly ceases, the overarching system cannot definitively determine if the asset reached its objective, was neutralized en route by kinetic fire, or succumbed to electronic interference.³⁴ This lack of granular data leads to profound inaccuracies in determining enemy attrition rates and forces commanders to make decisions based on highly flawed data sets.

6.2 The “Observer-Striker” Topology and Trailing Observers

To resolve these severe attribution gaps without relying on vulnerable centralized ISR assets, the swarm itself must adopt specialized, internal structural topologies. Rather than engineering every drone in the swarm to act solely as a kinetic effector, the swarm must autonomously designate specific platforms as trailing observers or organic BDA nodes.³⁵

This methodology involves explicitly pairing loitering munitions with dedicated surveillance drones within the swarm’s algorithmic structure.³⁶ By trailing an observer drone slightly behind a kinetic wave, the observer can continuously record the terminal trajectory of the munitions. Telemetry sensors on a striking munition can transmit its GPS coordinates and flight data up to the exact millisecond before detonation.³⁵ The trailing observer then analyzes the characteristics of the medium the munition passed through—for example, assessing whether a weapon successfully penetrated a reinforced bunker roof before detonating, or if it detonated harmlessly on the exterior.³⁵

This “observer-striker” methodology allows the swarm to establish a continuous, localized, and autonomous feedback loop. The observer assesses the initial kinetic wave, utilizes its multi-modal sensors to confirm exactly which targets were successfully destroyed, and instantly assigns remaining, un-engaged targets to the second wave of strikers. This creates a highly efficient one-to-one engagement ratio that conserves the swarm’s overall ammunition depth and minimizes unintended collateral damage, operating almost entirely independently of human oversight.²⁰

7. Updating Intelligence, Mission Command, and Situational Awareness

The ultimate operational purpose of Battle Damage Assessment is not merely to compile a post-action inventory of destroyed enemy equipment. The primary objective is to continuously update the commander’s understanding of the operational environment, enabling rapid, informed decision-making and facilitating the deployment of exploitation forces.⁷

7.1 The Disconnect in the Common Operating Picture (COP)

Mission Command is a foundational doctrine based on a hybrid of centralized control and decentralized execution.³² However, there is a recognized trade-off between the proximity of forces to tactical engagements and their access to different kinds of operational information.³² As distance from the forward edge of the battlefield increases, situational awareness regarding specific tactical engagements inherently decreases.³² Put simply, the farther a commander is from the front lines, the less granular their understanding of the immediate ground truth.

Commanders located in centralized Joint Operations Centers rely entirely on the COP to understand the disposition of forces. However, if a drone swarm acts autonomously and alters its targeting priorities based on its own edge-processed BDA, the COP immediately becomes desynchronized from reality. For instance, if a swarm of 300 drones is deployed against a confirmed enemy artillery battery, and the swarm’s organic intelligence nodes determine upon arrival that the battery is actually an elaborate decoy setup, the swarm may autonomously re-route to a pre-planned secondary target.³⁷ If this decision logic and the subsequent BDA findings are not efficiently and automatically communicated back to the enterprise level, the Joint Force Commander will continue to operate under the dangerously false assumption that the primary target was engaged and neutralized.

7.2 Vision-Language Models (VLM) for Automated Reporting

To bridge this critical intelligence gap without overwhelming the constrained bandwidth of the contested EMS, the DoD must invest heavily in integrating Vision-Language Models (VLM) into the drone enterprise architecture.¹⁰ VLMs possess the advanced algorithmic capability to ingest complex, multi-modal sensor data—such as fused SAR, thermal, and RGB imagery—and translate those visual inputs into actionable, human-readable intelligence insights.¹⁰

Instead of a human intelligence analyst reviewing hours of degraded drone footage to manually compile a Phase I BDA report, a VLM operating either at the tactical edge or at a localized forward relay node can instantly generate a formatted text report for transmission. Experimental results demonstrate that VLM components show strong semantic alignment, producing highly accurate translations of complex sensor data.¹⁰ A system could autonomously transmit a concise packet: “Assault on Grid Alpha complete. 12 of 15 air defense assets neutralized. 3 assets remain active. Swarm expended 80% of kinetic payload. Recommend follow-on artillery strike.”

This methodology ensures that high-level commanders maintain acute operational awareness and can effectively exercise Mission Command without the need to micromanage the swarm’s individual tactical engagements.³²

7.3 Data Formats, API Integration, and MLOps

Processing massed drone strike data requires a robust, scalable enterprise architecture that extends far beyond the physical airframe. The current methodology of retrieving data manually from returning platforms or relying on “swivel-chair” integration by analysts manually inputting data into the COP is unworkable at scale.³⁰

The enterprise architecture must incorporate:

• Modular Data Platforms: Systems capable of receiving diverse telemetry and sensor data from various drone manufacturers, formatting it, filtering it for relevance, and converting it for immediate ingestion into joint intelligence systems.³⁰
• API Integration: The GCS must seamlessly interface with broader military intelligence databases via Application Programming Interfaces (APIs), pushing BDA updates automatically so that all adjacent units and echelons are instantly aware of target status changes.³⁸
• Machine Learning Operations (MLOps): Edge AI models will inevitably encounter novel adversary countermeasures, new camouflage techniques, or unexpected environmental variables. The enterprise requires a continuous, automated MLOps pipeline to ingest post-mission data, retrain the computer vision and BDA models, and push updated algorithmic weights back to the swarm fleet before the next operational deployment.³³

8. Legal, Ethical, and Accountability Frameworks for Autonomous BDA

As BDA methodologies inevitably shift from human-in-the-loop PED architectures to edge-AI autonomous assessments, the DoD faces significant legal, ethical, and oversight challenges. Existing international law, particularly concerning the conduct of war, is heavily predicated on human accountability and conscious decision-making.⁸

8.1 Proportionality and the Assessment of Collateral Damage

Under the established Law of Armed Conflict (LOAC), military operations must strictly adhere to the rule of proportionality. This principle dictates that the incidental loss of civilian life, injury to civilians, or damage to civilian objects must not be excessive in relation to the anticipated concrete and direct military advantage of an attack.⁴⁰ Assessing proportionality requires a deeply contextual understanding of the operational environment—a nuanced cognitive capability that current AI systems struggle to reliably frame.⁴⁰

In a massed drone strike, if an autonomous system initiates an attack, it must inherently possess the capability to assess collateral damage post-strike to determine if further engagement violates LOAC. The deployment of autonomous explosive devices, such as the Shahed-136 loitering munitions used extensively against energy infrastructure in the Russo-Ukrainian war, highlights these dangers.⁸ These systems, classified as Lethal Autonomous Weapon Systems (LAWS), engage pre-selected target groups independently.⁸

If a U.S. swarm strikes a legitimate military target but causes unintended, cascading failures in adjacent civilian infrastructure, who is accountable? The inability of an autonomous system to “frame” and contextualize the broader environment may result in the system deciding to launch follow-on attacks based not merely on incomplete, but fundamentally flawed understandings of the circumstances.⁴⁰ If the swarm lacks robust, multi-modal BDA capabilities to realize it has caused excessive collateral damage, it lacks the necessary failsafes to halt its own operations.

8.2 The “Black Box” Problem and Systemic Traceability

The deployment of LAWS raises profound questions regarding how individuals or state actors answer for crimes or errors committed on a mass scale by autonomous entities.⁸ If a swarm executes a coordinated strike utilizing its own edge-assessed BDA to determine target validity and authorize kinetic deployment, the human operator is effectively removed from the kill chain.⁴¹

Without meticulous enterprise requirements to log the decision-making process of every single drone—often referred to as the “black box” problem for autonomous systems—attributing a kinetic effect to a specific algorithm or decision node becomes impossible. Process evidence must be derived from how data is prepared, managed, analyzed, and delivered throughout the flight lifecycle.³³

If an unlawful strike occurs, or a friendly fire incident takes place, investigators must be able to pull the BDA telemetry, the sensor logs, and the specific AI decision tree to determine the root cause. Was the error due to hardware sensor failure, algorithmic bias in the targeting model, or sophisticated adversary spoofing and deception? The current institutional rush to field vast quantities of attritable drones often overlooks the massive data storage, logging architectures, and forensic methodologies required to maintain this legal compliance and operational accountability.³³

9. Strategic Recommendations and Institutional Reform

The Department of Defense’s pursuit of drone dominance, catalyzed by the rapid innovations and harsh lessons observed in theaters like Ukraine and the Middle East ¹⁴, is a strategically necessary evolution. However, deploying mass without the institutional capacity to assess its impact is strategically hollow and operationally reckless. To ensure commanders possess accurate situational awareness, maintain compliance with international law, and retain the ability to dictate the tempo of modern conflict, DoD leadership must aggressively address the following methodological gaps.

9.1 Shift Investment Priorities from Platforms to Architectures

The acquisition focus must widen significantly from the procurement of individual, attritable drone platforms to the procurement of the underlying software, data pipelines, and sensing architectures. A swarm of 10,000 highly advanced drones is entirely neutralized if the enterprise cannot process their telemetry or conduct BDA in a severely jammed environment. Investment should heavily prioritize the development of ultra-fast neural networks, neuromorphic computing, and multi-modal sensor fusion algorithms (specifically integrating SAR, EO, and IR) that operate reliably at the tactical edge.⁹ The software that assesses the strike is as vital as the hardware that delivers it.

9.2 Establish Composite Formations to Reduce Sensor-to-Shooter Latency

Operational latency expands unacceptably when detection systems, kinetic shooters, EW cells, and BDA analysts operate in separate, stovepiped organizational stacks.²² The DoD must develop composite formations that co-locate and institutionalize the integration of these complementary capabilities at the brigade and battalion levels.²² Drone defense and employment cannot be siloed exclusively to dedicated air defense or aviation units; every unit must possess organic, integrated capabilities to launch, assess, and iterate upon unmanned strikes.³

9.3 Codify Autonomous BDA Methodologies in Doctrine

Joint Publication 3-60 and supporting multi-service tactics, techniques, and procedures must be comprehensively revised to reflect the realities of the modern, automated battlefield.¹⁸ Doctrine must move beyond the centralized, human-dependent Phase I-III BDA processes and formally establish frameworks for automated, probabilistic edge assessment. Furthermore, doctrine must establish clear, standardized guidelines for when a commander is authorized to rely on AI-generated BDA to approve follow-on fires, explicitly addressing the inherent risks of algorithmic deception and false positives.

9.4 Mandate Systemic Traceability and Forensic Logging

To resolve the ethical and legal ambiguities surrounding massed autonomous strikes, the DoD must implement strict, non-negotiable enterprise requirements for data logging. Every drone within a deployed swarm must act as a distinct node that continuously records its sensor inputs, target selections, and BDA conclusions.³³ This methodology ensures that kinetic effects can be accurately attributed, AI behaviors can be audited post-mission, and compliance with the Law of Armed Conflict can be rigidly maintained, even when the human operator is no longer present in the immediate tactical loop.

By aggressively addressing these systemic requirements to design, build, operate, and evolve the BDA enterprise, the Department of Defense can successfully transform massed drone swarms from a blunt instrument of attrition into a highly precise, intelligent, and strategically decisive capability for the future Joint Force.

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1. Executive Summary

The United States Department of Defense (DoD) is actively pursuing a fundamental paradigm shift in its approach to force projection. Driven by the imperative to offset the mass and capacity advantages of near-peer adversaries, the DoD has prioritized the rapid acquisition and fielding of thousands of all-domain attritable autonomous (ADA2) systems.¹ Initially operationalized through the Replicator initiative and subsequently evolving into the broader, heavily funded Defense Autonomous Warfare Group (DAWG) ³, this strategic vector seeks to overwhelm adversary anti-access/area-denial (A2/AD) networks using low-cost, expendable platforms.¹ However, the prevailing discourse surrounding mass unmanned aerial systems (UAS) operations suffers from a severe analytical blind spot: an overwhelming fixation on the digital, aerodynamic, and software-defined capabilities of the platforms, coupled with a systemic disregard for the physical logistics required to project them into a contested theater of operations.

Attritable systems are frequently conceptualized by defense planners as intangible, software-driven assets. In reality, fielding thousands of uncrewed platforms generates a colossal, highly sensitive, and dangerous physical footprint. This report outlines the systemic logistical requirements and constraints that dictate the feasibility of mass drone operations. The analysis reveals that the primary bottlenecks to these initiatives will not be autonomous swarming software or airframe manufacturing capacity, but rather the severe volumetric inefficiency of shipping fragile electronics, the stringent regulatory constraints governing the global transport of Class 9 hazardous lithium-ion batteries, and the immense power generation and climate-controlled storage requirements placed on austere forward operating bases.

Leadership must recognize that a drone’s operational weight is entirely distinct from its logistical weight. Its protective packaging, associated launch systems, and ground support equipment multiply its footprint exponentially. Furthermore, due to mandatory aviation transport regulations requiring lithium batteries to be shipped at a state of charge (SOC) below 30% ⁵, these platforms arrive at the tactical edge effectively incapacitated. This dynamic shifts the burden of energy generation directly to forward units, demanding industrial-scale charging infrastructure that relies heavily on vulnerable Class III bulk fuel supply chains.⁶

To ensure that thousands of ADA2 platforms can reliably reach and operate within contested environments, DoD planning must pivot from a platform-centric acquisition model to a logistics-first sustainment architecture. The ability to mass autonomous forces is entirely contingent on the United States Transportation Command (USTRANSCOM), the Military Sealift Command (MSC), and the tactical energy networks of deployed units.⁷ This report provides a comprehensive overview of the physical, regulatory, and infrastructural realities that currently threaten to throttle the deployment of mass autonomy.

2. The Strategic Context of Mass Autonomy and the Illusion of Scale

The modern battlefield is undergoing a rapid evolution, driven by the proliferation of networked, autonomous, and semi-autonomous systems. The Replicator initiative, launched in 2023 by the Defense Innovation Unit (DIU), represents a deliberate endeavor to integrate commercial-scale autonomous production with military operations.² The first iteration, Replicator 1, focuses on fielding thousands of aerial, ground, and maritime platforms by late 2025, while Replicator 2 pivots toward countering small unmanned aerial systems (C-sUAS).¹⁰ Selected platforms for these initial tranches include AeroVironment’s Switchblade 600, Anduril’s Altius-600, and the Ghost-X.²

To sustain and expand these efforts, the DoD has transitioned the underlying principles of Replicator into the Defense Autonomous Warfare Group (DAWG), signaling a massive financial commitment with nearly $55 billion allocated for research, development, and procurement in the coming fiscal cycles.³ The strategic appeal of these systems lies in their classification as “attritable”—platforms engineered and manufactured affordably enough that combatant commanders can tolerate a much higher degree of risk in their tactical employment.⁴

2.1 The Divergence Between Manned and Unmanned Logistics

The concept of attritability, while operationally advantageous, creates a psychological disconnect regarding logistics. Because the platforms are intended to be lost in combat ², there is a pervasive assumption that their supply chain is equally frictionless and expendable. This is a fundamental fallacy. An attritable drone requires the exact same meticulous supply chain handling, climate controls, and hazardous material processing as a non-attritable, multi-million-dollar precision-guided munition.

When procuring conventional manned aircraft, the DoD heavily scrutinizes the logistics tail. Platforms like the MQ-9 Reaper, which has amassed over two million flight hours, are supported by deeply entrenched, highly evolved logistical networks featuring dedicated runways, sophisticated hangars, and predictable maintenance schedules.¹³ Crucially, manned systems and large medium-altitude long-endurance (MALE) drones self-deploy; they fly from their point of origin to the theater of operations.

Attritable tactical drones, conversely, are classified as cargo. They do not fly to the fight; they must be boxed, palletized, trucked, flown via strategic airlift, offloaded, and distributed via tactical ground vehicles. Consequently, procuring a fleet of 10,000 small drones imposes a fundamentally different, and arguably more complex, strain on the Defense Transportation System (DTS) than sustaining a squadron of manned fighters.¹⁴ If the DoD attempts to scale drone procurement without proportionally scaling the physical infrastructure required to transport, store, and power them, the result will be localized logistical paralysis. Pallets of drones will inevitably become stranded at aerial ports of embarkation (APOEs) due to hazard restrictions, or they will arrive at forward bases that lack the electrical capacity to charge them.

3. Volumetric Inefficiency: Airframes, Fragility, and Packaging Standards

The most immediate physical constraint of mass drone deployment is the mathematical relationship between the drone’s operational dimensions and its required shipping volume. Modern tactical drones are meticulously optimized for aerodynamics and payload capacity, resulting in lightweight, fragile, and often awkwardly shaped airframes. To survive the extreme rigors of the military supply chain—which includes extreme temperature fluctuations, moisture, vibration, mechanical shock, and rough terrain handling—these systems must be packaged according to stringent, unyielding military specifications.

3.1 MIL-STD-2073-1E and the Reality of Level A Packing

The preservation, packaging, packing, and marking of military supplies are governed by(https://quicksearch.dla.mil/qsDocDetails.aspx?ident_number=37232), which dictates the methods required to protect materiel against environmentally induced degradation during multiple handling events in the DTS.¹⁴ Tactical drones, categorized as highly sensitive electronics with low fragility factors (frequently rated at less than 50 Gs of shock tolerance), require Level A military packing.¹⁶ Level A is the highest level of protection, mandated for items intended for long-term storage or deployment in austere, wartime environments.

Under these standards, a bare drone cannot simply be placed in a standard cardboard box. Level A packaging requires that the item be placed in individual, non-metallic inner packaging.¹⁸ This inner layer must be surrounded by specific cushioning material—such as foam-in-place (FIP) polyurethanes or specialized fast-pack inserts (PPP-B-1672)—that is non-combustible, electrically non-conductive, and highly absorbent.¹⁶ Furthermore, the entire cushioned assembly is often sealed within waterproof and vapor-proof barrier bags before being secured inside robust exterior shipping containers, such as wood-cleated panelboard boxes (ASTM-D-6251) or heavy-duty reusable molded containers.¹⁷

This mandatory preservation process creates massive volumetric inefficiency. A tactical drone’s weight is relatively negligible, but its “cube”—the cubic volume of its compliant shipping crate—is massive. The military logistics enterprise operates on the physical limitations of pallets and containers, and drones consume this space at an alarming rate.

3.2 Platform Loadout Metrics and the All-Up Round

Examining the specific physical dimensions of the platforms selected for the initial phases of the Replicator initiative illustrates this volumetric trap:

• AeroVironment Switchblade 600: This extended-range loitering munition is designed for precision strikes against armored targets.²² The bare munition itself weighs 15 kg (33 lbs) and has a length of 1.3 meters (51 inches).²² However, the Switchblade is shipped and deployed as an All-In-One Tube-Launched System. The All-Up Round (AUR), which includes the protective launcher tube and integrated firing hardware, weighs 29.5 kg (65 lbs).²² The Level A packaging required to protect this 1.3-meter AUR further increases the gross weight and significantly expands the volume.
• Anduril Altius-600: The ALTIUS-600 has a length of 1 meter and a base weight of 12.2 kg (27 lbs).²⁵ It is deployed from a pneumatic launch container.²⁶ While the airframe is lightweight, the rigid launch tube and the necessary foam-in-place cushioning demand substantial cargo space.¹⁹
• Anduril Ghost-X: Selected for the Army’s Company Level Small Unmanned Aircraft System (sUAS) Directed Requirement ²⁷, this system provides expeditionary surveillance. While highly capable, its complex rotor systems and delicate optics require extensive physical protection during transit to prevent misalignment.

When Air Force loadmasters build standard 463L pallets for strategic airlift, they are constrained by a usable base of 104 by 84 inches and a maximum height of 96 inches. Because drone crates cannot be stacked infinitely due to crush hazards and delicate center-of-gravity constraints, a single 463L pallet that could theoretically hold 10,000 pounds of dense artillery ammunition or water might only hold a few dozen attritable drones weighing a fraction of that amount. The DoD is effectively consuming its most premium strategic transportation asset to fly empty space and protective foam across the ocean.

4. The Class 9 Hazard: Lithium Battery Transport Regulations

While volumetric inefficiency restricts the amount of cargo space available, lithium-ion batteries present an acute, hard-stop regulatory constraint that dictates how, when, and if these platforms can be moved at all.

Every modern electric tactical UAS relies on high-energy-density lithium-ion or lithium-polymer batteries to achieve necessary flight times and payload capacities.²⁸ Under both international civilian law and strict military regulations, lithium batteries are universally classified as Class 9 Hazardous Materials.³⁰ Depending on their configuration, they are categorized as UN3480 (for standalone lithium-ion batteries) or UN3481 (for lithium-ion batteries packed with or contained in equipment).³² The transportation of these assets is heavily scrutinized and restricted due to the severe risk of thermal runaway—a catastrophic internal short-circuit chain reaction that causes intense, self-sustaining fires that are highly resistant to standard aviation fire suppression systems.³³

4.1 Air Transport Restrictions and AFMAN 24-204

The strategic airlift of these batteries is governed by a complex web of overlapping authorities, primarily the International Air Transport Association (IATA) Dangerous Goods Regulations and the Air Force Joint Manual (AFMAN 24-204), “Preparing Hazardous Materials for Military Air Shipment”.⁵

To mitigate the existential risk of an in-flight thermal runaway event, current regulations mandate that rechargeable lithium batteries must be shipped at a State of Charge (SOC) not exceeding 30% of their rated capacity.⁵ Furthermore, there are stringent limits on the maximum net quantity of lithium batteries permitted per cargo aircraft compartment.³³ For certain configurations and sizes, the maximum net quantity per cargo aircraft can be as low as 35 kg.³¹ While waivers and exceptions exist for national security movements under 49 CFR 173.7, the baseline safety protocols dictate severe segregation and quantity limits to ensure that an oxygen-starved cargo hold or automated fire suppression system can actually contain a potential blaze.³³

This regulatory environment creates a profound logistical bottleneck for the mass deployment envisioned by the Replicator initiative:

1. Compartment Saturation: A C-17 Globemaster III cannot simply be filled floor-to-ceiling with attritable drones. Load planners must meticulously distribute the UN3480/UN3481 hazardous materials across different isolated cargo compartments to avoid exceeding strict Class 9 net quantity limits.³³ Consequently, if a specific drone model carries a heavy battery payload for extended endurance, the aircraft may “hazmat out” (reach its legal hazardous material weight limit) while the physical cargo bay remains largely empty.
2. Dead on Arrival Logistics: Because batteries must legally and safely be shipped at less than 30% SOC ⁵, drones arriving in the theater of operations are not combat-ready. They cannot be rapidly offloaded from a transport aircraft and immediately launched to counter an advancing adversary. They must first be routed through a logistical node, unpacked, and fully recharged. This operational reality completely transfers the burden of operational readiness directly onto the theater’s tactical power grid, introducing devastating delays to force projection timelines.

4.2 Packaging Integrity and Retrograde Complexities

The dangers of Class 9 materials are amplified when dealing with damaged systems. If an attritable drone is damaged during transit, rough handling, or limited operations and requires retrograde transport for depot-level repair or forensic analysis, the battery must be isolated. Damaged or defective batteries face even stricter protocols, requiring individual non-metallic inner packaging surrounded by non-combustible, electrically non-conductive cushioning, with explicit exterior markings denoting the heightened hazard.¹⁸ Furthermore, it is strictly prohibited to mix hazardous and non-hazardous solid waste in the same package.⁵ Managing this retrograde process across thousands of deployed systems drastically complicates reverse logistics, requiring forward units to act as highly trained hazardous material processing centers. The Commercial Vehicle Safety Alliance (CVSA) has even recommended that the Department of Transportation reclassify lithium batteries from Class 9 to a more restrictive Division 4.3 material due to these runaway thermal reaction risks, which would further tighten future airlift and ground transport requirements.³⁷

5. Strategic Airlift and Sealift Constraints

The ability to successfully mass autonomous forces is entirely contingent on the capacity and readiness of the United States Transportation Command (USTRANSCOM). The physical realities of volumetric packaging and Class 9 hazardous materials translate directly into severe, structural strain on strategic mobility assets.

5.1 The Strategic Airlift Deficit

USTRANSCOM relies on a validated requirement of a 275-aircraft organic strategic airlift fleet to meet national defense objectives and global contingency plans.⁸ The C-17 Globemaster III serves as the backbone of this fleet, providing rapid, inter-theater mobility. However, strategic airlift is fundamentally designed and optimized for high-value, high-density, time-sensitive cargo.

When the DoD demands the rapid, simultaneous deployment of thousands of high-cube, low-density attritable drones—each packed in expansive protective crates and subject to compartment-specific lithium battery limits—the airlift architecture is forced to operate at maximum inefficiency. In a crisis scenario, drones will compete directly for premium pallet space against critically needed precision munitions, medical supplies, and ground vehicle repair parts. In a contested logistics environment, where speed equates directly to deterrence ⁸, dedicating vast swaths of C-17 capacity to transport empty space and packing foam represents an unacceptable tactical trade-off.

Intra-theater lift faces similar structural pressures. The Air Force’s retirement of older C-130H aircraft has reduced the tactical airlift fleet from over 500 aircraft in 2003 to a congressionally mandated inventory of 271.⁸ Distributing massive quantities of volumetric drone crates from major theater hubs to dispersed forward operating bases using a constrained C-130 fleet will inevitably result in operational delays. While innovative concepts like the Rapid Dragon program—which successfully demonstrated the deployment of palletized munitions directly from C-17 and EC-130 aircraft ³⁸—show promise for direct aerial delivery, these systems still consume vast amounts of cargo volume and require extensive rigging.

5.2 The Atrophy of Strategic Sealift

Historically, when airlift capacity is constrained or reserved for immediate priorities, the DoD relies heavily on the Military Sealift Command (MSC) to transport approximately 90 percent of U.S. Army and Marine Corps equipment into the theater of operations.⁹ For the true mass deployment of drone fleets, utilizing standard International Organization for Standardization (ISO) containers via sealift is the only mathematically viable method.

However, the strategic sealift enterprise is currently facing an unprecedented readiness crisis. Current assessments indicate that MSC readiness levels have dropped to an alarming 59 percent, driven primarily by vessel age and deteriorating material condition.⁹ The sealift fleet is projected to lose between 1 million and 2 million square feet of capacity annually as legacy ships reach the end of their useful service lives.⁹

Furthermore, in a pacing scenario against a near-peer adversary such as China, sea lines of communication (SLOCs) from the continental United States to the Indo-Pacific will be heavily contested.⁹ Transporting mass quantities of Class 9 lithium batteries via sealift also invokes the International Maritime Dangerous Goods (IMDG) Code, which mandates robust, fire-resistant packaging, adequate cushioning, and strict stowage segregation.⁵

The systemic requirement is unavoidable: The Replicator initiative cannot rely solely on the C-17 fleet for initial deployment. The DoD must urgently integrate mass drone packaging into standard ISO container dimensions—specifically utilizing TRICON (8’x8’x6.5′), BICON (8’x8’x10′), and QUADCON (8’x6.10’x4.9′) steel-framed containers ³⁹—that are engineered for hazardous material sealift, and combatant commanders must factor the extended transit times of maritime logistics into their operational plans.

6. Forward Operating Base Footprint: Storage and Climate Control

Upon successfully navigating the strategic airlift or sealift pipeline and reaching the theater of operations, the logistical burden transitions entirely from USTRANSCOM to the gaining tactical units. The pervasive assumption that thousands of attritable drones can simply be unloaded, unboxed, and stacked in a general-purpose, unconditioned supply tent completely ignores the volatile chemistry of lithium-ion technology and the fragility of the platforms.

6.1 The Aggregate Risk of Thermal Runaway

Aggregating thousands of lithium-ion batteries at a Forward Operating Base (FOB) introduces a profound vulnerability to the installation. A single thermal runaway event—whether caused by mechanical damage during rough transport, a latent manufacturing defect, or an enemy kinetic strike—can rapidly propagate to adjacent stored batteries. This creates an uncontrollable, self-sustaining chemical blaze that conventional firefighting techniques and standard water suppression systems cannot easily extinguish.³⁴

Military history provides stark precedents. The Navy’s Naval Surface Warfare Center (NSWC) Carderock Division has documented incidents where standard military lithium batteries, such as the widely used BB 2590, entered thermal runaway while stored in an Army vehicle-mounted shelter, causing massive damage to surrounding equipment and exposing personnel to severe hazard.³⁴

6.2 Specialized Climate-Controlled Infrastructure

To prevent spontaneous degradation, maintain operational capacity, and prevent thermal events, lithium-ion batteries must be stored in specific, highly regulated environmental conditions. Both industry standards and DoD best practices dictate that these batteries must be kept at stable temperatures, generally below 80°F (26.6°C).⁴¹ In austere, high-temperature operational environments—such as the Middle East or the Indo-Pacific during summer months—maintaining this temperature requires dedicated, power-hungry climate control systems running continuously.

Standard canvas tents or rudimentary plywood structures are entirely inadequate. Adequate storage requires purpose-built facilities, such as commercial DrumLoc buildings or the military’s specialized CLASSIC (Containerized Lithium-ion Battery Storage and Sustained Intelligent Charging) containers developed by NSWC.³⁴ These specialized hazardous material bunkers require robust, integrated safety features, including:

• Explosion-proof electrical accessories, switches, and interior lighting.
• Active clean-agent fire suppression devices (e.g., FM 200 systems) tailored for chemical fires.⁴¹
• Advanced sensors capable of detecting chemical off-gassing, temperature spikes, or smoke prior to a full thermal runaway event.³⁴
• Passive physical mitigation measures, such as internal blast walls, to prevent failure propagation between stored units and to direct the blast outward rather than upward into the facility.³⁴

Consequently, deploying a mass drone capability does not merely require runway space or a clear patch of dirt; it necessitates the deployment of heavy, specialized ISO containers acting as forward hazardous material bunkers, which in turn require constant, uninterrupted power to run their HVAC and automated sensor systems.

7. Industrial-Scale Power Generation at the Tactical Edge

The most critical, yet systematically overlooked, operational requirement of mass drone deployment is tactical power generation. As previously established, drones arrive in theater at less than 30% SOC due to strict aviation transport regulations.⁵ Before a single swarm can be launched to achieve the mass effects envisioned by Replicator, the entire fleet must be charged. This transforms a forward drone unit into an industrial power consumer.

7.1 The Mathematics of Megawatt Demand

Commercial and military drone operations are exceptionally energy-intensive. A single tactical drone team conducting persistent intelligence, surveillance, and reconnaissance (ISR) or kinetic strike operations can easily cycle through 10 to 12 battery charges per day, consuming approximately 2 to 3 kilowatt-hours (kWh) of electricity daily.⁴² Scaling this baseline to the DoD’s vision of fielding “multiple thousands” of autonomous platforms ⁴ creates a staggering localized power demand.

If a combatant commander intends to launch a coordinated wave of 1,000 drones within a narrow operational window, those batteries must be charged simultaneously. Fast-charging a single heavy-lift or long-range tactical drone battery requires a dedicated draw of between 150W and 300W of continuous power.⁴³ In parallel, the necessary ground support equipment—including operator laptops, network routers, data relays, and GPS base stations—draws a continuous 100W to 250W per station.⁴³

When operating multiple systems simultaneously during pre-mission staging, the peak electrical demand rapidly surges into the tens of thousands of watts per tactical node.⁴³ Standard consumer-grade portable power stations, small solar arrays, or vehicle-mounted inverters are vastly insufficient for this industrial scale.

7.2 The Vulnerability of Tactical Generators and Class III Logistics

Historically, the default solution to remote, off-grid power demand has been the towed gasoline or diesel generator.⁶ However, relying on traditional internal combustion generators to power mass drone operations presents severe tactical liabilities that undermine the very purpose of the capability:

1. Acoustic and Thermal Signatures: In an era of advanced multi-spectral ISR, the massive noise pollution and intense thermal bloom of a large generator farm immediately compromise the position of the drone launch site.⁶ Adversary sensors will detect the power generation node long before the drones are launched, inviting preemptive kinetic strikes.
2. Vibration Interference: Micro-vibrations emanating from heavy diesel generators can interfere with the delicate calibration of drone targeting optics and sensitive charging equipment, leading to high failure rates before takeoff.⁶
3. Contested Class III Logistics: Generators burn vast quantities of fuel. Moving thousands of gallons of Class III bulk fuel to remote launch sites requires vulnerable, highly visible convoy operations. This reliance on a heavy logistics tail defeats the strategic purpose of utilizing distributed, low-risk attritable forces.

7.3 The Hazards of Parallel Charging

To save time and meet aggressive operational tempos, drone operators frequently utilize parallel charging—connecting multiple lithium-polymer (LiPo) batteries to a single high-output charger via a parallel charging board.⁴⁵ While highly efficient for rapid turnarounds, parallel charging introduces acute fire risks if not managed with absolute precision.

Batteries connected in parallel must possess the identical cell count and very similar starting voltages (typically within 0.1V of each other).⁴⁵ If a depleted battery is hastily connected in parallel with a partially charged battery, the voltage differential causes a massive, uncontrolled rush of current into the depleted battery, frequently resulting in catastrophic cell failure, explosions, and fires.⁴⁷ Furthermore, charging at higher currents (e.g., 2c instead of the safer standard 1c rate) drastically increases the wear on the battery and the risk of thermal events.⁴⁸ Managing this delicate, mathematically precise charging process across thousands of rapidly degrading attritable batteries in a chaotic combat environment requires sophisticated Battery Management Systems (BMS) ²⁹ and highly trained personnel, which inherently slows the tempo of operations.

8. Maintenance Footprint, Personnel, and Training Readiness

The very term “attritable” implies expendability and a short lifecycle, which often leads to the dangerous assumption that these platforms require little to no maintenance or human support. In reality, assembling, calibrating, launching, and managing a fleet of attritable UAS requires a highly specialized human capital footprint and expansive physical facilities.

8.1 Assembly and Facility Square Footage

Most tactical drones, to save volumetric space during strategic airlift, are shipped partially disassembled within their protective MIL-STD crates.⁴⁹ Upon arrival at the FOB, they must be meticulously unpacked, assembled, firmware-updated, and flight-checked before they can be assigned to a mission. Establishing a forward drone assembly and maintenance facility requires significant physical space that must be factored into base planning.

Basic institutional standards for drone laboratories and maintenance facilities dictate a minimum of 750 square feet solely for assembly and maintenance areas, with 10-to-12-foot ceilings to accommodate wingspan clearances and component testing.⁵⁰ Storage rooms capable of holding just 500 drones require upwards of 1,000 square feet of dedicated, secure shelving.⁵⁰ When scaling to the DAWG and Replicator goal of multiple thousands of systems, commanders will require massive, semi-permanent structures—such as large clamshell tents or repurposed aircraft hangars—simply to process the unboxing and assembly of the hardware. This vast physical requirement directly contradicts the operational goal of maintaining a light, agile, and geographically dispersed expeditionary footprint.⁵¹

8.2 The Human Capital Constraint

Unmanned systems, paradoxically, are highly manpower-intensive on the ground. A complex UAV operation often requires at least seven crewmembers dedicated to specific tasks: takeoff and landing procedures, in-flight monitoring, payload operation, and flight line maintenance.⁵² While the Replicator initiative explicitly aims to leverage advanced autonomy to allow a single operator to control multiple vehicles in a swarm configuration ¹, the physical handling, battery swapping, and maintenance of the drones remains a heavily manual task.

To manage the unprecedented battery and maintenance logistics, the DoD will need to fundamentally restructure its forward support companies (FSCs) and sustainment brigades.⁵³ Battlefield energy generation and distribution nodes must be established within Light Support Battalions (LSB) and Division Sustainment Support Battalions (DSSB).⁷ This shift requires existing 91D (Generator Mechanic) and 94-series personnel to undergo extensive retraining to manage complex hybrid and lithium-ion systems, diagnosing battery health and managing parallel charging racks.⁷

Furthermore, training the sheer number of operators required to employ thousands of drones demands a massive expansion of the institutional training base. Simulator training is essential for building initial flight proficiency and mitigating crash risks.⁵⁴ The Marine Corps, for example, is heavily reliant on enterprise-resourced simulation capabilities delivered via the Marine Common Virtual Platform (MCVP)—such as DART 2.0 and FlowState—as well as commercial simulators like Velocidrone, to provide service-wide training solutions.⁵⁴ Scaling this training pipeline to match the procurement of the hardware is a multi-year endeavor.

8.3 Supply Chain Security and Parts Replacement

Finally, forward units cannot simply rely on localized procurement or commercial replacement of broken drone parts to sustain operations. Due to strict supply chain security mandates, such as the Federal Acquisition Supply Chain Security Act (FASC), there is a blanket prohibition on the use of FASC-prohibited unmanned aircraft systems and associated elements.⁵⁵ This legislation ensures that no components sourced from adversarial nations (such as certain Chinese-manufactured motors or flight controllers) can be integrated into DoD networks. Consequently, every spare propeller, servo, and circuit board must be sourced through secure, vetted, and often severely backlogged military supply chains. This reality forces deployed units to stockpile vast quantities of authorized spare parts at the FOB to maintain readiness, further increasing the logistical cube and storage requirements.

9. Strategic Conclusions and Required Leadership Action

The United States Department of Defense possesses the unparalleled technological prowess to design, develop, and manufacture thousands of highly capable autonomous systems. The massive financial commitments to the DAWG and Replicator initiatives guarantee that the industrial base will produce the hardware. However, the true measure of mass autonomy’s success will not be determined by factory output or lines of code, but by the Defense Transportation System’s ability to project those assets globally without buckling under the weight of archaic packaging standards, hazardous material laws, and localized power deficits.

To ensure that mass drone operations transition from a theoretical strategic concept to a viable, reliable tactical reality, leadership must immediately acknowledge and aggressively mitigate the physical logistics footprint. The analysis indicates several critical areas for immediate, systemic action:

1. Redesign Military Packaging for Drones: The DoD must collaborate directly with defense contractors to engineer MIL-STD-2073 compliant shipping configurations that drastically reduce the “cube.” Drones should be designed with foldable, robust components that minimize the need for expansive foam-in-place cushioning, maximizing the density of a 463L pallet and ISO containers. The packaging must be considered as important as the payload.
2. Modernize the Class 9 Hazardous Pipeline: USTRANSCOM and the Defense Logistics Agency (DLA) must develop streamlined, pre-approved hazardous material transport corridors specifically optimized for lithium-ion batteries. To bypass the airlift bottleneck, the DoD must invest heavily in specialized ISO containers (analogous to the CLASSIC system) that can transport, safely store, and simultaneously charge batteries at the tactical edge, relying more heavily on proactive strategic sealift.
3. Elevate Energy to a Primary Supply Class: The Brigade Support Operations (SPO) and unit S4s must immediately begin treating electrical power forecasting with the exact same rigor and priority as Class III (Fuel) and Class V (Ammunition) sustainment.⁷ The DoD must rapidly procure modular, silent, and high-capacity battlefield energy storage networks to decouple deployed drone units from vulnerable liquid fuel supply chains and noisy tactical generators.

The technology of mass autonomy is profound, and its potential to deter aggression is immense. Yet, it remains inextricably tethered to the physical world. By shifting the strategic focus toward the unglamorous realities of logistics, volumetric packaging, hazardous materials, and tactical power generation, DoD leadership can ensure that the autonomous systems built to win the next conflict are actually capable of reaching the battlefield.

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