1. Executive Summary

The proliferation and sustainment of uncrewed aerial systems (UAS) across the battlefields of Ukraine, supported by the defense industrial bases of the Russian Federation and the Islamic Republic of Iran, represent a structural shift in the character of modern warfare. This shift is definitively characterized by the transition from the artisanal, low-volume deployment of high-end precision-guided munitions to the industrialized mass production of low-cost, high-impact robotic systems.¹ The ongoing conflict provides a real-time, unprecedented laboratory for military strategists to observe how state and non-state actors sustain high-intensity drone combat under the immense pressures of international sanctions regimes, constrained global supply chains, and rapidly evolving tactical countermeasures.

The sustainment of these combat systems is no longer solely a function of advanced aerospace engineering or exquisite platform survivability; rather, it is dictated by supply chain agility, the aggressive integration of commercial off-the-shelf (COTS) components, and the ruthless optimization of the cost-to-attrition ratio.³ Extensive analysis of the operational models employed by Ukraine, Russia, and Iran reveals three highly distinct paradigms of sustainment, each reflecting the unique geopolitical constraints and domestic industrial capacities of the respective actor. Ukraine exemplifies a decentralized, networked, and digitally integrated model heavily reliant on civilian crowdfunding, startup ecosystems, and frontline technical adaptation.⁶ Conversely, Russia demonstrates a state-directed, centralized industrialization model capable of absorbing foreign technology and scaling it through massive capital expenditures and the mobilization of imported labor.⁸ Iran illustrates an operationally resilient, “decentralized mosaic” production model capable of rapid iteration and sustained manufacturing output despite direct military strikes and severe international economic sanctions.¹¹

Furthermore, the sustainment of drone combat exposes critical and potentially systemic vulnerabilities within the global supply chain. The structural dependency on specific chemical and metallurgical raw materials—such as carbon fiber, lithium, and rare-earth magnets predominantly sourced from or refined in the People’s Republic of China—creates strategic chokepoints that adversaries can, and have, leveraged.¹² Simultaneously, the persistent discovery of Western microelectronics in Russian and Iranian weapon platforms underscores the glaring limitations of traditional export control regimes in an era where dual-use commercial technologies dominate the battlespace.³ This report systematically examines the logistics, economics, supply chain dynamics, and organizational doctrines that enable the sustainment of drone combat, providing actionable insights for future force design, defense industrial base (DIB) strategy, and international export control enforcement.

2. The Strategic Landscape and the Axis of Aggressors

Before dissecting the specific mechanics of drone sustainment, it is necessary to contextualize the geopolitical framework that facilitates the flow of technology, capital, and materiel between the belligerent actors. Military strategists must recognize that the sustainment of the Russian war effort is not occurring in a vacuum; it is the product of an interconnected web of strategic threats and alliances.¹⁵

The defense industrial cooperation between Russia and Iran, heavily facilitated by Chinese economic and technological infrastructure, represents the operationalization of a rising authoritarian alignment.¹⁵ This alignment—frequently characterized by analysts as an “Axis of Aggressors” or an “Axis of Evasion”—consists of Russia, China, North Korea, and Iran.¹⁵ These states, while possessing divergent political systems and long-term regional objectives, are unified by a shared strategic intent to contest, complicate, and ultimately roll back the power and influence of the United States and its democratic partners.¹⁵

The sustainment of drone combat in Ukraine is a primary vector through which this axis operationalizes its intent. The deployment of Iranian-designed Shahed loitering munitions by Russian forces is not merely a tactical battlefield expedient; it is a calculated effort to utilize low-cost, asymmetric means to degrade a Western-backed military force and exhaust advanced Western air defense infrastructure.¹¹ Furthermore, this cooperation is highly reciprocal. In exchange for the continuous supply of unmanned systems and the establishment of domestic production facilities, Russia provides Iran with advanced military technology, diplomatic cover, and capital, while China facilitates the evasion of Western sanctions by providing a vast market for sanctioned hydrocarbons and serving as the primary conduit for dual-use microelectronics.¹⁵

The integration of North Korea into this matrix further solidifies the depth of this logistical alliance. Field investigations by(https://www.conflictarm.com/publications/) have documented the physical presence of North Korean submunitions, which underwent localized modifications to serve as payloads for weaponized First-Person View (FPV) drones utilized by Russian forces in Ukraine.¹⁷ This cross-pollination of munitions, platforms, and labor across the axis demonstrates a robust, collective defense industrial depth that significantly complicates efforts to interdict the supply chains sustaining the conflict.¹⁷

3. The Attritional Economics of Unmanned Systems

The sustainment of modern drone warfare is fundamentally governed by the economics of attrition. Unlike legacy aerospace platforms—such as manned fighter aircraft or strategic bombers, which prioritize survivability, multi-role capability, and long operational lifespans—the tactical drones defining the Ukraine conflict are explicitly designed for mass deployment, high attrition rates, and single-use lethality.² The strategic utility of these unmanned systems is derived not from their individual technical sophistication or survivability, but from their collective ability to impose vastly disproportionate economic and material costs on the adversary’s defensive infrastructure.⁴

3.1 The Interceptor Cost Asymmetry and Saturation Tactics

The introduction of the Iranian-designed Shahed-131 and Shahed-136 loitering munitions (designated Geran-1 and Geran-2 by the Russian military) into the European theater established a highly favorable, asymmetrical cost-exchange ratio for the attacking force.⁴ The Shahed platform is characterized by its intentional simplicity, utilizing a basic fiberglass body, a commercially available engine (such as the Mado MD550), and an unguided or basic GPS-guided navigation system.²² This simplicity keeps the unit cost exceptionally low, estimated by military analysts at approximately $35,000 per drone.⁴

Against this low-cost, mass-produced threat, defending forces are frequently compelled to deploy highly sophisticated, low-density, and exquisitely expensive interceptors.²³ For example, a single Patriot (PAC-3) interceptor costs over $3 million, while a National Advanced Surface-to-Air Missile System (NASAMS) utilizing the AIM 9-X variant costs over $1 million per missile.⁴ Even when factoring in the high interception rate historically achieved by Ukrainian integrated air defenses, the economic logic remains undeniably sound for the attacker.

Statistical analysis of Russian strike data indicates that Shahed drones successfully strike their intended targets less than 10 percent of the time.⁴ However, because of their low unit cost, Russia can afford to launch mass salvos on a near-daily basis. The estimated cost for the Russian military to successfully strike a target utilizing precision bombardment with Shahed-type drones is roughly $350,000 per target struck.⁴ In stark contrast, a successful strike utilizing a conventional, high-end Russian munition, such as the Kh-22 cruise missile, costs the Russian state approximately $1 million per target struck.⁴

This cost-exchange dilemma constitutes a strategic “tax” imposed on defending forces.⁴ The mass salvos are deliberately designed to saturate radar screens, exhaust critical interceptor stockpiles, and force command centers to make difficult triage decisions regarding the deployment of limited surface-to-air missile (SAM) assets. By cluttering the airspace, the attacker creates temporal windows of vulnerability, allowing more capable, high-end ballistic and hypersonic missiles to penetrate the defensive umbrellas.⁴ Furthermore, the introduction of non-kinetic decoys alongside genuine attack drones artificially inflates the number of threats on radar, further diluting the defender’s resources and significantly improving the attacker’s overall operational cost-effectiveness.⁴

3.2 FPV Drones, Naval Drones, and the Obsolescence of Heavy Platforms

The economic asymmetry observed at the strategic air defense level extends forcefully to the tactical ground and maritime domains through the mass proliferation of First-Person View (FPV) drones and uncrewed surface vessels (USVs). FPV drones represent the ultimate convergence of commercial video gaming technology and lethal military application, transforming the battlefield with highly agile, manually piloted systems.²⁴

The manufacturing costs of FPV drones perfectly illustrate the economic threat to legacy systems. FPV drones are routinely manufactured for approximately $400 to $500, with self-assembled variants utilized by decentralized units occasionally dropping below the $200 threshold.²⁵ Despite this negligible financial footprint, FPVs are capable of delivering specialized warheads—such as repurposed rocket-propelled grenades (RPGs) or custom-built thermobaric charges—with the precision necessary to exploit the top-down vulnerabilities of heavy armor.¹⁹

When comparing these costs, the implications for military sustainment are stark. An Excalibur precision-guided artillery round costs approximately $100,000, while a modern Infantry Fighting Vehicle (IFV) costs between $3 million and $4 million, and a Main Battle Tank (MBT) costs between $2 million and $10 million.²⁶ The cost ratio of a $500 drone destroying a multi-million-dollar tank represents an astronomical 2,000-to-1 advantage for the attacker.²⁵ This structural reality suggests a highly durable cost-imposition model where cheap, iterative offensive systems force continuous, expensive defensive adaptations.²⁶ In this environment, raw industrial depth and the capacity to rapidly generate cheap mass become more decisive than the sophistication or armor plating of a single legacy platform.²

This dynamic is equally prevalent in the maritime domain. Ukraine’s naval drone campaign, utilizing platforms such as the Sea Baby and MAGURA uncrewed surface vessels, has demonstrated how relatively inexpensive assets can challenge conventional naval supremacy.⁵ These USVs have successfully struck at least eleven Russian vessels, including a Kilo-class submarine and multiple tankers operating within Russia’s sanctions-evading shadow fleet.²⁵ The destruction or disablement of oil tankers transporting cargo valued at nearly $70 million, using drones that cost a fraction of that amount, highlights the unsustainable economic burden placed on operators of large, conventional platforms when forced to defend against asymmetric drone swarms.²⁵

To sustainably combat these threats over the long term, defending militaries must fundamentally realign their economic defensive posture. The current reliance on multi-million dollar interceptors must be supplemented by directed-energy weapons. Systems currently in development and early deployment, such as Israel’s Iron Beam (a 100-kilowatt ground-based laser system costing approximately $3 to $5 per shot) or the United Kingdom’s DragonFire (a 50-kilowatt system costing approximately £10 per shot), represent the only economically viable path to defeating the attritional logic of drone warfare.²⁵

4. The Ukrainian Paradigm: Decentralized Innovation and Digital Logistics

To meet the insatiable demand for unmanned systems, Ukraine has engineered a sustainment model defined by radical decentralization, rapid iterative innovation, and an unparalleled reliance on civilian tech integration.⁵ Lacking a massive, state-owned defense conglomerate capable of meeting immediate wartime demands at the onset of the conflict, Ukraine fostered a unique “crowdfunding war” dynamic, effectively mobilizing commercial technology sectors, volunteer organizations, and startup ecosystems.²

4.1 Crowdsourced Acquisition and the DOT-Chain Ecosystem

The Ukrainian model relies heavily on non-governmental funding and civil society initiatives to sustain frontline units. Initiatives such as “Operation Unity”—a high-profile collaboration between the state-run(https://u24.gov.ua/operation-unity) fundraising platform, the Come Back Alive foundation, and the digital bank monobank—have successfully crowdsourced hundreds of millions of hryvnias.⁷ One specific iteration of this initiative established a goal of 220 million UAH to procure 5,000 FPV drones, specifically allocating 157 million UAH for 3,000 drones equipped with thermal imaging cameras for night operations, 34 million UAH for 2,000 drones with daylight cameras, and 29 million UAH for Ukrainian-made cumulative and high-explosive warheads.⁷ This direct civilian-to-military pipeline bypasses the sluggish, bureaucratic cycles that traditionally plague defense procurement.

To manage this complex, decentralized ecosystem of donors, manufacturers, and end-users, the Ukrainian Ministry of Defense implemented advanced digital command and control tools. The introduction of the DOT-Chain digital system represents a paradigm shift in military logistics. DOT-Chain introduces a needs-based procurement model where individual combat units function as autonomous consumers within a secure digital ecosystem.⁶ This system provides an aggregated, real-time view of demand across the entire front, linking manufacturers directly with operators and creating a highly responsive supply chain with shared visibility and accountability.⁶

4.2 Industrial Scaling and the Application of Wright’s Law

The production scaling achieved through this decentralized, software-defined model is staggering. The trajectory of Ukrainian manufacturing exemplifies a remarkable adherence to Wright’s Law, wherein the cost of production steadily declines as cumulative output scales and manufacturing processes are optimized.²⁵

From an initial base of approximately 800,000 systems manufactured in 2023, the domestic industrial apparatus expanded its output to 2.2 million units in 2024.³⁰ Projections for 2025 indicate a baseline production capability of 4 million units, with the Ukrainian Ministry of Defense establishing a strategic, long-term target of an unprecedented 7 million units for 2026.² To fund this massive scale-up, Ukraine estimates it requires $120 billion, with $60 billion sourced internally and via EU loans, prioritizing 80% of these funds for UAV production, air defense, and artillery.³⁰ By contrast, the United States currently manufactures approximately 100,000 combat drones annually, highlighting the sheer disparity in industrial mobilization.³⁰

[Image: Graph illustrating the exponential scaling of Ukrainian drone production from 2023 to projected 2026 targets alongside cost-reduction curves]

4.3 Tactical Edge Sustainment and Structural Dependencies

Sustaining millions of drones requires more than just manufacturing; it requires robust field maintenance. The attrition rate of tactical drones is exceptionally high. Aside from direct kinetic interception, drones are routinely lost to electronic warfare (EW) jamming, spoofing, battery exhaustion, and mechanical failure.⁵ Consequently, the ability to rapidly repair and cycle drones back into combat is a critical metric of unit effectiveness.

Ukraine has optimized its field sustainment through the deployment of mobile engineer workshops and electronic laboratories positioned directly behind the forward line of own troops (FLOT).³¹ Priced at approximately $36,000 each, these highly mobile, decentralized facilities allow drone units to perform emergency repairs, swap damaged motors, and implement software patches in a matter of hours, rather than sending equipment back to centralized depots.³¹ This agility is essential in an environment where the technological advantage between electronic warfare systems and drone communication frequencies shifts on a weekly basis.³¹

However, this model possesses inherent structural dependencies. The Ukrainian war effort is deeply tethered to commercial technologies to compensate for material inferiority against the Russian state. Ukrainian operations are structurally dependent on commercial satellite communications (such as Starlink), civilian navigation systems, and Earth-observation networks.⁵ The combat effectiveness of the Ukrainian forces relies heavily on software integration layers, such as Kropyva and Delta, which evolved from volunteer-driven applications into federated combat management ecosystems linking sensors to shooters.⁵ This reliance on commercial bearers means that any disruption in civilian service provision immediately degrades military capability.

5. The Russian Paradigm: Centralized Capital, State Absorption, and Labor Mobilization

In stark contrast to Ukraine’s decentralized approach, the Russian Federation operates a highly centralized, state-directed industrial model.² While the Russian military initially lagged in grassroots tactical innovation—often relying on rigid doctrines that hindered rapid adaptation—its centralized system proved highly capable of identifying successful asymmetric technologies, absorbing them into the formal defense industrial base, and applying massive state capital to achieve overwhelming scale.⁸

5.1 The Alabuga Special Economic Zone and Institutional Scaling

The Russian paradigm of drone sustainment is best illustrated by the development of the Alabuga Special Economic Zone (SEZ) in the Republic of Tatarstan. Following a franchise and technology transfer agreement with Iran, the Alabuga facility was established to localize the production of Shahed-131 and Shahed-136 loitering munitions.⁸

The facility rapidly scaled its operations, transitioning from the initial assembly of Iranian-provided knockdown kits to full domestic manufacturing of the drone airframes.³⁴ Demonstrating the depth of the Russian military-industrial complex, Alabuga outsourced the specialized production of warheads to established Russian chemical enterprises. To meet aggressive production goals, the facility contracted with the Scientific Research Institute of Applied Chemistry for 3,000 thermobaric warheads and with JSC NPO Basalt for 5,000 fragmentation-high explosive-incendiary warheads.³⁴ Internal project documentation indicated a long-term production goal of 10,000 Shahed-136 units, significantly higher than the initial 6,000-unit contract negotiated with Tehran.³⁴

5.2 Demographic Engineering and Labor Mobilization

The primary constraint on Russia’s centralized scaling model is not capital or raw materials, but human capital. To staff the massive Alabuga complex amid broad domestic labor shortages—exacerbated by military mobilization and casualties—the Russian state implemented aggressive, non-traditional recruitment strategies.⁹

The workforce, which expanded to over 25,000 employees by mid-2025, was rapidly augmented by recruiting students (some reportedly underage) from the local Alabuga Polytechnic institute.⁹ Furthermore, the facility established the “Alabuga Start” program, an international recruitment drive targeting young female migrant workers primarily from Africa, Latin America, and South Asia.⁹ To further bolster output, Russian commentators have discussed the integration of up to 25,000 highly motivated North Korean workers into the SEZ, a move that would effectively double the workforce and significantly increase the daily production rate of 90 Shahed drones.¹⁸

This mobilization of international labor is indicative of a broader shift toward “defense Keynesianism” within the Russian economy, where economic growth is driven almost entirely by military-related production.¹⁰ The defense sector has expanded to employ approximately 3.8 million people—roughly 5% of the total Russian workforce—drawn by salaries that often reach 150,000 rubles ($1,870) per month, nearly double the national median wage.¹⁰

5.3 The Evolution of the Lancet Munition

Concurrently, established Russian defense firms have evolved their product lines to dominate the tactical airspace. ZALA Aero Group, the manufacturer of the Lancet loitering munition, represents the successful institutionalization of drone warfare within the Russian military.³⁶

Valued at approximately $35,000 to $37,000 per unit, the Lancet has undergone continuous iterative upgrades based on extensive battlefield feedback.³⁶ Recent variants, specifically the Izdeliye 51 and 52 (and the related Chernika-2), have integrated larger payloads. For example, newer Lancets have replaced the standard 3 kg KZ-6 warhead with the 4.9 kg PTM-3 Soviet-designed anti-tank mine, allowing for more effective strikes against armored targets.³⁷ Crucially, to counter intense Ukrainian electronic warfare, ZALA Aero has integrated advanced autonomous target recognition utilizing machine vision and AI algorithms, allowing the munition to track and strike targets even in completely GPS-denied or heavily jammed environments.³⁸ The operational range of these upgraded systems has also been extended from roughly 40 kilometers to over 100 kilometers, allowing Russian forces to strike deep into the Ukrainian tactical rear.³⁸

5.4 Doctrinal Rigidity and the Risks of Centralization

While industrial output is immense, the Russian model struggles with operational doctrine. The integration of mass drone capabilities forced changes in military structure. In 2024, the Russian 2nd Combined Arms Army initiated the “Drone Line” project, establishing specific echelons of drone operators tasked with targeting enemy logistics, allocating up to 560 UAS per day to specific units.³² Russia subsequently established a dedicated branch of the armed forces focused entirely on unmanned systems, seeking to centralize development, training, and operational command.⁴⁰

However, this drive for absolute centralization presents a distinct operational vulnerability. Analysts consistently note that the extreme effectiveness of drone units stems from their tactical decentralization and operational independence.⁴¹ By aggressively disbanding informal volunteer detachments and forcing agile drone operators into rigid, centralized military hierarchies—often assigning highly specialized pilots to traditional infantry assault roles to backfill manpower shortages—the Russian Ministry of Defense risks degrading the very agility that makes drone warfare effective.⁴¹ In the rapidly evolving offense-defense race of drone combat, overly rigid command and control structures slow the innovation cycle, limiting the ability of frontline troops to react to sudden shifts in the adversary’s electronic warfare posture.²

6. The Iranian Paradigm: Decentralized Mosaic and Strategic Resilience

The Islamic Republic of Iran plays a critical architectural role in sustaining the drone capabilities of the Axis of Aggressors. Iran’s model of drone sustainment is fundamentally designed around survivability and strategic resilience, characterized by a “decentralized mosaic” production strategy.¹¹

6.1 Institutional Infrastructure and Design Philosophy

The Iranian drone program is overseen by the Ministry of Defense and Armed Forces Logistics (MODAFL) and the Islamic Revolutionary Guard Corps (IRGC) Aerospace Force.¹¹ Within this structure, specialized entities drive development. The Shahed Aviation Industries Research Center (SAIRC), located near Isfahan, functions primarily as a design bureau, developing the blueprints for the Shahed-131, Shahed-136, and Mohajer series.²¹ The designs are then handed over for series production to the Iran Aircraft Manufacturing Industrial Company (HESA), a state-owned subsidiary of MODAFL that has historically maintained, repaired, and reverse-engineered various military aircraft.⁴⁵

The fundamental design philosophy of Iranian drones is centered on simplicity and manufacturability.²² By utilizing basic fiberglass bodies, commercially available dual-use engines, and rudimentary guidance systems, Iranian engineers have created platforms that do not require highly specialized aerospace manufacturing environments.²²

6.2 The Mosaic Strategy and Operational Survivability

To protect this industrial base from the persistent threat of aerial bombardment and sabotage by the United States and Israel, Iran disperses its command structures, weapon systems, and manufacturing nodes across vast geographic areas and subterranean facilities.¹¹ This “Decentralized Mosaic Defense” strategy ensures that military functions can continue seamlessly even under intense attack.¹¹

Because the drones are relatively simple to construct, they can be assembled in rudimentary, dual-use facilities—such as civilian speedboat repair shops—ensuring that production can continue even when primary, state-owned aerospace facilities like HESA are targeted or disrupted.²² Demonstrating the extreme resilience of this model, senior Iranian military officials reported a tenfold increase in the production rate of attack drones in the months following the intense June 2025 conflict with Israel, signaling a robust and highly adaptable capacity to replenish attrited stockpiles under fire.⁴⁸

6.3 Proliferation as a Strategic Weapon

The decentralized mosaic model not only protects domestic production but also facilitates the rapid transfer of technology to proxy forces and allied nations. Iran has successfully exported its drone manufacturing methodologies to Russia (via the Alabuga SEZ) and to the Houthi forces in Yemen.⁸ The ability to package drone designs, commercial component lists, and basic assembly instructions into exportable “franchises” provides Iran with significant geopolitical leverage, allowing it to sustain low-cost, high-impact proxy wars across multiple theaters simultaneously.⁸

7. The Architecture of Evasion: Global Supply Chains and Microelectronics

The industrialized production of drones across all three paradigms—Ukrainian, Russian, and Iranian—relies upon an incredibly complex global supply chain. Despite unprecedented multilateral sanctions imposed by the Global Export Control Coalition (GECC), both Russia and Iran have successfully maintained their supply lines by systematically exploiting structural gaps in global commerce.³

7.1 Structural Dependencies on Western Technology

A defining feature of the current conflict is the persistent, structural reliance on Western-manufactured commercial off-the-shelf (COTS) technologies. Extensive field investigations and teardowns by organizations such as Conflict Armament Research (CAR) and the Independent Anti-Corruption Commission (NAKO) have repeatedly documented the presence of advanced Western components inside downed Russian and Iranian platforms.¹⁴

The “Terror in the Details” report published by NAKO highlighted that the “brains” and “eyes” of systems like the Shahed-136—including microprocessors, Ethernet transceivers, semiconductors, and memory modules—frequently originate from prominent technology corporations headquartered in the United States, Japan, Canada, and Switzerland.¹⁴ The discovery of components manufactured by Intel Corporation, AMD, and Texas Instruments within these weapon systems has led to civil lawsuits accusing distributors, such as Mouser Electronics, of “willful ignorance” in allowing restricted chips to reach Russian shell companies.⁵² Similarly, the Russian Lancet drone relies heavily on the Jetson TX2 module by NVIDIA and the Zynq SoC module by AMD/Xilinx for its onboard control and programmable logic.³⁶

Because these items are widely used in civilian electronics, automotive manufacturing, and telecommunications, they have historically been viewed by export-control regimes as low-risk.³ This ubiquitous commercial availability creates profound information gaps. While Original Equipment Manufacturers (OEMs) may maintain strict compliance protocols, the independent distributors and brokers who facilitate secondary and tertiary market sales rarely possess the capability or the legal mandate to enforce stringent end-user verification.³

7.2 The Mechanics of the Shadow Supply Chain

To acquire these restricted technologies, Russia and Iran rely on a geopolitical “Axis of Evasion,” heavily anchored by China and facilitated by a network of intermediary states.¹⁶ Procurement networks construct multi-layered webs of shell companies, utilizing weak enforcement jurisdictions to obscure the ultimate destination of the hardware.³

China serves as a primary enabler within this system. In addition to importing sanctioned oil, China provides a vast marketplace for sophisticated dual-use technology, including navigation systems and critical components, facilitating their transfer to Tehran and Moscow.¹⁶ For instance, intelligence reports indicate that Iranian companies, such as Pars Aero, manage logistics and external transactions through Hong Kong-registered entities like Foxtech Hobby to purchase critical drone parts under civilian product labels, only to later repurpose them for military applications.⁵³

Similarly, comprehensive trade data analysis reveals that Kazakhstan serves as a critical regional conduit for routing export-restricted semiconductors into the Russian economy.⁵⁴ Following the imposition of sanctions, Kazakhstan’s exports of microelectronics to Russia surged by over 567%, acting as a strategic bypass for the Russian defense industry.⁵⁴

These illicit procurement networks operate with astonishing speed and agility. Forensic analysis of an Iranian Shahed-136 recovered in Ukraine in April 2023 revealed a component manufactured in China just three months prior, in January 2023.³ This rapid integration cycle underscores the immense difficulty of interdicting supply chains that operate via commercial courier networks rather than traditional, easily monitored military logistics vessels.

7.3 The “Friction Tax” on the Aggressors

While these evasion efforts are successful in maintaining drone production lines, they impose a severe “friction tax” on the target nations. Rerouting supplies through regional allies and paying premiums to smugglers significantly raises the procurement costs and lead times for components.⁵⁴ This financial and logistical strain forces the Russian military to strictly prioritize the production and repair of tactically relevant assets—specifically mass-produced drones and armored vehicles—while neglecting the maintenance of more complex, legacy platforms, such as advanced tactical aviation, which suffer from severe spare parts shortages.⁵⁴

8. The Chemical and Metallurgical Foundation: Critical Raw Materials

Beyond the complex architecture of microelectronics, the sustainment of drone combat is inextricably linked to the physical materials required for their construction. The mass production of affordable unmanned systems demands continuous, uninterrupted access to specialized chemistry and metallurgy.¹² In this domain, both the allied defense industrial base and the adversarial networks face severe structural vulnerabilities linked to Chinese supply chain hegemony.

The material dependency of a modern military drone rests on four key pillars ¹²:

The strategic vulnerability of this material dependency is not theoretical; it has already been actively weaponized. In late 2024, Chinese state entities capitalized on their dominance by holding back vital battery cell shipments intended for major Western drone manufacturers (such as the US firm Skydio), while simultaneously redirecting production capacity to sustain the Russian war effort.¹³ Furthermore, China has initiated restrictions on the export of germanium, a material crucial for the thermal sensors that allow drones to operate effectively at night, and has heavily scrutinized the flow of permanent magnets into the European theater.¹³

Unless strategic reserves are significantly expanded and allied refining capabilities are developed domestically or via secure partnerships, the capacity of Western militaries to sustain the “affordable mass” required for modern warfare will remain beholden to adversary-controlled supply chains.¹² In a protracted conflict, a shortage of specialized metallurgical inputs could handicap warfighting capacity just as severely as a shortage of finished munitions.

9. Battlefield Ripples: Logistics, Medicine, and Tactical Evolution

The saturation of the airspace with persistent, low-cost surveillance and strike drones has fundamentally altered the physical and cognitive reality of the battlefield. The immediate tactical rear is no longer a sanctuary, and operations previously considered routine now carry extreme risk.

9.1 The Transparent Battlefield and Logistics

The ubiquity of drones has rendered the modern battlefield entirely transparent.¹ This transparency has catastrophic implications for traditional logistics and sustainment operations. Wheeled transport columns attempting to supply forward positions are routinely detected and destroyed by FPV swarms before reaching their destinations. In response, both sides have been forced to innovate. Uncrewed ground vehicles (UGVs) and heavy-lift multicopters (such as the Ukrainian “Baba Yaga” drone) are increasingly utilized for vital last-mile resupply, dropping ammunition, batteries, and rations to forward trench lines that are completely inaccessible to manned vehicles due to the omnipresent threat of aerial strikes.²

9.2 Medical Sustainment in the Drone Era

Furthermore, the drone threat has severely compromised traditional medical evacuation (MEDEVAC) protocols. The concept of the “golden hour”—rapidly evacuating wounded personnel to advanced medical facilities—is often impossible when wheeled ambulances and armored medical transports serve as highly visible targets for loitering munitions.⁵⁵

In response, Ukrainian medical commands have adapted by constructing a decentralized network of underground “stabilization points” located perilously close to the front lines.⁵⁵ These hardened, subterranean facilities, often utilizing repurposed Cold War infrastructure or rapid new construction, represent a forced shift toward “prolonged field care”.⁵⁵ Medics must now be trained to sustain critically wounded casualties for extended periods in austere environments, accepting that rapid evacuation is frequently impossible under drone saturation.⁵⁵ This adaptation underscores how the proliferation of one specific technology forces systemic restructuring across the entire spectrum of military operations, from logistics to combat medicine.

9.3 The EW Arms Race and Cognitive Warfare

The tactical evolution of drones is driven by a continuous, high-speed arms race with electronic warfare (EW) systems. As defenders deploy localized jammers to sever the command links of incoming FPVs, attackers rapidly adapt. This offense-defense cycle led to the emergence of fiber-optic cable drones in 2024, which spool a physical wire behind them as they fly, rendering them entirely immune to radio frequency jamming and spoofing.⁵ The subsequent integration of machine learning and Artificial Intelligence (AI) for autonomous terminal guidance further reduces the reliance on vulnerable communication links.⁵

Beyond the physical destruction, analysts emphasize the profound cognitive dimension of drone warfare. The incessant buzzing of loitering munitions, the unpredictability of FPV strikes, and the constant exposure to aerial surveillance create intense psychological strain, shaping morale and decision-making at both the tactical and operational levels.²

10. Strategic Mitigation and Future Force Design

The sustainment models and tactical realities observed in Ukraine, Russia, and Iran provide a definitive blueprint for the character of mid-21st-century warfare. The fusion of mass-produced commercial technology with lethal payloads has fundamentally and irreversibly altered operational planning.² For strategic planners, military leadership, and defense industrial bases, several urgent lessons must be internalized and actioned:

1. Realigning the Economics of Air Defense: The current paradigm, which relies on multi-million dollar interceptors to defeat highly expendable, low-cost drones, is strategically and economically untenable for long-term sustainment.²³ Future force design must ruthlessly prioritize layered, integrated air and missile defense systems that incorporate non-kinetic effectors (such as advanced, portable electronic warfare and microwave disruption) and cost-effective hard-kill solutions (such as directed-energy laser systems and automated gun platforms).²⁰ Establishing a favorable, or at least parity-level, cost-exchange ratio in the defensive sphere is an existential requirement.

2. Cultivating Industrial Agility and Supply Chain Sovereignty: The ability to produce highly sophisticated, exquisite platforms in peacetime must be balanced with the latent capacity to mass-produce “good enough” systems during a protracted conflict.² Western defense bases must transition away from exclusively artisanal, long-lead-time manufacturing toward modular designs that permit rapid scaling.² Crucially, this requires securing sovereign or allied access to the critical raw materials—lithium, rare-earth magnets, and specialized composites—that serve as the chemical and metallurgical foundation of modern autonomous systems.¹²

3. Embracing Hybrid Procurement Architectures: The success of Ukraine’s decentralized, software-defined procurement ecosystem (DOT-Chain) and its reliance on civilian tech integration demonstrates that the modern defense sector can no longer operate in a bureaucratic silo.⁵ Future conflicts will require militaries to rapidly absorb commercial innovation, shorten acquisition cycles from years to mere weeks, and push maintenance, repair, and modification capabilities directly to the tactical edge.²

The conflict in Ukraine has definitively proven that the denial of airspace, facilitated by the relentless sustainment of cheap, networked autonomous systems, is often more decisive than the pursuit of absolute traditional air superiority.¹ Success in future peer-level engagements will favor the actor capable of marrying the agility of commercial innovation with the deep industrial capacity required to sustain mass on the modern battlefield.

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Sources Used

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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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