1. Executive Summary

The character of modern warfare is undergoing a profound transformation driven by the rapid proliferation, integration, and continuous evolution of uncrewed aerial systems (UAS). As the United States Department of Defense (DoD) prepares for massive investments in drone technology, a critical strategic vulnerability remains under-addressed by military and defense planners: the speed at which peer and near-peer adversaries observe, adapt to, and counter technological advantages. While domestic defense discussions frequently fixate on the acquisition of exquisite platforms and the expansion of domestic drone fleets, the operational reality dictates that the platform itself is merely the most visible component of a highly complex, multidomain capability. The ongoing conflicts in Eastern Europe and the Middle East serve as real-world laboratories, demonstrating that advantage in modern combat is no longer strictly derived from possessing the most advanced technology at the onset of hostilities. Instead, military advantage is increasingly dictated by the speed of the adaptation cycle—the ability to field a capability, observe the enemy’s countermeasures, and deploy a counter-countermeasure before the adversary can institutionalize their defense.¹

In these contemporary operational environments, the development cycle for adversary countermeasures has compressed from years to months, and in some tactical scenarios, to mere days.³ This report synthesizes national intelligence and military analysis to outline the systemic requirements necessary for the DoD to design, build, operate, and evolve UAS capabilities in a highly contested environment. It assesses the overlooked mechanisms of enemy adaptation, particularly the rapid exploitation and reverse-engineering of captured U.S. technology, and the fielding of advanced electronic warfare (EW) and directed energy (DE) countermeasures.⁵ Furthermore, this assessment details the organizational agility required to transition from a static, platform-centric procurement model to a dynamic, continuous capability-evolution model.

The traditional paradigm of military technological superiority relies on a linear process of research, development, testing, and fielding, often spanning a decade or more. Once a system is fielded, it is expected to provide an asymmetric advantage for years before an adversary develops a viable countermeasure. The proliferation of commercial-off-the-shelf (COTS) drone technology, combined with the democratization of digital command and control networks, has shattered this paradigm.² To achieve decision dominance and outpace the adversary adaptation cycle, the DoD must rethink its approach to supply chain resilience, spectrum management, tactical-edge fabrication, and the integration of artificial intelligence into command and control architectures.⁸ The central thesis of this report is that the United States military must weaponize the learning cycle itself, transitioning to an organizational model capable of deploying updates, counter-measures, and hardware modifications at operationally relevant speeds.¹

2. The Accelerating Adversary Adaptation Cycle

The operational environment has shifted definitively toward an era characterized by precise mass, where adversaries utilize large volumes of low-cost, expendable uncrewed systems to overwhelm sophisticated, legacy defense networks.² Within this paradigm, the most significant threat is not the initial capability of the adversary’s drone swarm, but rather the speed at which the adversary organization learns, adapts, and implements changes based on operational contact with U.S. and allied forces.

The Shift to Adaptation in Contact

The concept of Adaptation in Contact describes a closed-loop learning cycle where operational engagement generates immediate technical data, which is then used to rapidly update tactics, software, electromagnetic signatures, and hardware configurations.¹ Validated changes are deployed back to the tactical edge before the adversary can fully react. By applying this infrastructure for adaptation, a military force can turn deterrence into a measurable control problem, running calibrated moves to observe adversary responses and learning which changes reliably create uncertainty without waiting for a systemic crisis.¹

In the Russo-Ukrainian conflict, tactical adaptation occurs at an unprecedented velocity. Russian forces are observed altering their drone flight routes, antenna configurations, and guidance methods every few weeks to bypass Ukrainian electronic warfare bubbles.² When traditional radio-frequency (RF) links are successfully jammed, adversaries quickly transition to fiber-optic tethers or autonomous terminal guidance driven by machine vision, rendering sophisticated RF jammers obsolete.⁶ This iterative process means that a brilliant tactical innovation or a new highly capable drone model provides only a fleeting advantage. A capability fielded on Monday may be neutralized by Friday if the organization lacks the infrastructure to push software updates or modular hardware changes continuously.³

This compression of the innovation cycle challenges the foundational assumptions of the U.S. defense acquisition process. The Joint Capabilities Integration and Development System (JCIDS), optimized for acquiring exquisite, highly survivable platforms over multi-year timelines, is structurally incapable of matching the pace of Adaptation in Contact.¹³ Consequently, the DoD frequently fields systems that are technologically advanced but tactically outdated upon deployment, as adversaries have already observed prototypes, mapped electromagnetic signatures, and developed appropriate countermeasures.

The Adversary Entente and Collaborative Knowledge Sharing

The speed of adaptation is heavily compounded by the emergence of a collaborative adversary learning bloc—primarily comprising the Russian Federation, the People’s Republic of China (PRC), the Islamic Republic of Iran, and North Korea.² This network functions as a connected knowledge market where strategic and tactical lessons derived from contact with Western systems are rapidly disseminated, creating a compounding effect on military innovation.

The relationships between these nations have evolved past transactional arms sales into deeply integrated technical cooperation. For instance, the battlefield data gathered by Russian forces regarding the vulnerabilities of U.S. precision-guided munitions and drones to specific EW frequencies is almost certainly analyzed in conjunction with Chinese technical experts.¹⁶ China, possessing the world’s most extensive commercial drone manufacturing base, provides the component supply chain—including mesh networking modems, navigation sensors, and specialized microelectronics—that allows Russia and Iran to modify their systems to bypass newly discovered Western defenses.¹⁸

This two-way partnership ensures that when the U.S. military deploys a new platform or countermeasure in one theater, the defensive solutions developed against it will quickly proliferate to adversaries in entirely different geographic commands.²Iranian operational concepts, such as launching mixed salvos of inexpensive drones and ballistic missiles to saturate air defenses, are refined based on Russian combat experience and enabled by Chinese industrial capacity.²⁰Consequently, U.S. forces must anticipate that any operational deployment of a new UAS capability will instantly trigger a distributed, multinational effort to identify its vulnerabilities and reverse-engineer its strengths.

3. Technical Exploitation and the Speed of Reverse Engineering

A frequent oversight in U.S. strategic planning is the underestimation of the timeline required for peer adversaries to capture, reverse-engineer, and operationalize advanced uncrewed systems. The U.S. military has historically relied on the complexity of its systems to serve as a barrier to exploitation. However, the modular nature of modern drone technology, combined with the adversary’s willingness to integrate COTS components into military airframes, has drastically reduced the friction of reverse engineering.

Historical Precedents and the Compression of Exploitation Time

The operational history of U.S. drone deployments reveals a consistent pattern of adversary technical exploitation. The most prominent example occurred in December 2011, when an American Lockheed Martin RQ-170 Sentinel stealth drone was captured largely intact by Iranian forces utilizing electronic spoofing and cyberwarfare techniques.²² Despite initial Western assessments that Iran lacked the industrial base to fully exploit the technology, Iranian aerospace organizations successfully decoded the data and reverse-engineered the platform.²³ This effort directly resulted in the production of the Shahed 171 Simorgh and Shahed Saeqeh—jet-powered, flying-wing combat drones that incorporate the radar-evading geometry of the original U.S. platform while utilizing Iranian and Chinese internal components.²⁴

Similarly, the capture of U.S. ScanEagle drones led to Iranian domestic production lines that subsequently supplied proxy forces across the Middle East, effectively turning U.S. surveillance assets into adversary strike capabilities.²³ China has exhibited similar capabilities over decades, having previously reverse-engineered Israeli Harpy loitering munitions to produce the ASN-301 ²⁶, and having utilized components of downed U.S. target drones to jumpstart its early UAV programs, such as the Chang Kong-1 and WZ-5.⁵

The success of these reverse-engineering efforts relies on a strategy of pragmatic integration. Adversaries do not attempt to perfectly replicate every U.S. microchip or proprietary software algorithm. Instead, they clone the aerodynamic properties and structural designs, and then populate the airframe with commercially available, unregulated components.²⁹ The Iranian Shahed-136, for example, utilizes a delta-wing design paired with a reverse-engineered German Limbach L 550 engine, navigated by Chinese GNSS modules.²⁹ This modular approach bypasses complex manufacturing bottlenecks and accelerates the time from capture to deployment.

The Modern Formalized Exploitation Pipeline

Today, the exploitation pipeline is highly formalized and significantly faster. Adversaries have established dedicated intelligence and engineering task forces whose sole purpose is to recover downed Western UAS, extract the encrypted firmware, analyze the communication protocols, and identify hardware supply chain origins.³¹

The implications are severe for programs that aim to mass-produce attritable drones. If the U.S. deploys thousands of autonomous systems into contested airspace, a statistically significant number will inevitably fall into enemy hands intact—either through kinetic disablement, EW forced-landings, or mechanical failure.³³ Once captured, adversaries do not necessarily need to replicate the entire system to defeat it. By analyzing the drone’s logic boards, sensor suites, and navigation algorithms, adversary engineers can identify the exact frequency hopping patterns, optical recognition parameters, and autonomous decision trees the drone relies upon.³⁵

This technical intelligence allows the adversary to calibrate their EW jammers and directed energy weapons specifically to the vulnerabilities of the U.S. swarm within weeks of the platform’s initial deployment. Furthermore, the integration of advanced artificial intelligence into cyber operations has dramatically reduced the time required to find and exploit software vulnerabilities. As demonstrated by recent developments in frontier AI models capable of autonomously discovering zero-day vulnerabilities and generating working exploits without human guidance, the timeline for software exploitation has shifted from months to days.³⁷ If an adversary captures an uncrewed system with vulnerable firmware, the associated attack paths can be mapped and disseminated across the adversary entente almost instantaneously, rendering entire fleets of U.S. drones susceptible to hijacking or disruption.

4. Overlooked Adversary Countermeasures: Electronic Warfare

The proliferation of cheap, autonomous UAS has fundamentally altered the economics of air defense. The U.S. has historically relied on highly capable, exquisite kinetic interceptors to defeat aerial threats. However, when a defender is forced to expend a $3 million Patriot missile or a $100,000 Stinger missile to destroy a $30,000 Shahed-136 or a $500 commercial quadcopter, the attacker wins the economic exchange ratio regardless of the tactical outcome.¹⁰ Adversaries acutely understand this cost asymmetry and are deliberately fielding drone swarms to bankrupt defender magazines and exhaust logistical supply lines.

While the U.S. military has recognized this challenge and initiated the development of cost-effective countermeasures, there remains a systemic failure to appreciate how rapidly peer adversaries—specifically China and Russia—are deploying their own non-kinetic defenses, particularly electronic warfare, to neutralize incoming U.S. uncrewed systems.

The Maturation of Adversary Electromagnetic Warfare

Russia and China view the electromagnetic spectrum (EMS) not merely as an enabling environment, but as a primary maneuver space and warfighting domain, and have invested heavily in EW capabilities to deny U.S. forces access to it.³⁹ The Russian deployment of EW in Ukraine has been characterized as the densest electromagnetic environment in modern military history, exposing the vulnerabilities of systems reliant on persistent connectivity.¹¹

Adversary EW strategies focus on disrupting the crucial links that uncrewed systems rely upon: GPS/GNSS navigation signals, satellite communications, and operator command-and-control (C2) data links. By projecting high-powered interference, Russian systems have successfully blinded the navigation suites of highly sophisticated Western precision-guided munitions—such as Excalibur artillery shells and HIMARS rockets—rendering them tactically ineffective and forcing a rapid reevaluation of their utility.¹¹

Against drone swarms, adversary EW aims to sever the connection between the drones and their operators, or between the drones themselves. Without resilient mesh networking and autonomous fallback protocols, a drone swarm subjected to severe broadband jamming will lose cohesion, drift off course, or trigger automatic landing protocols, effectively neutralizing the threat without the adversary firing a single kinetic shot.⁴² The sheer volume of EW activity also creates a secondary problem: the degradation of Identity Friend or Foe (IFF) systems. In a saturated airspace, defenders struggle to distinguish between incoming adversary attack drones and returning friendly autonomous assets. This “duck test” ambiguity reduces response times and has directly contributed to fatal incidents where U.S. forces failed to engage hostile drones due to confusion with friendly signatures.⁴³

The Cat-and-Mouse Game of Spectrum Dominance

The struggle for electromagnetic dominance is not static; it is a continuous cycle of measure and countermeasure. When Ukrainian forces successfully employed portable EW systems to spoof the navigation of Russian Shahed drones, Russian engineers quickly adapted.⁴⁴ To counteract localized jamming, Russian forces began integrating Chinese-made mesh modems for resilient radio links, installing controlled reception pattern antennas (CRPA) to filter out interference, and utilizing mothership drones to relay signals to first-person-view (FPV) attack drones operating deep in the rear.¹⁸

The most significant adaptation has been the shift away from the electromagnetic spectrum entirely. To bypass the densest EW bubbles, adversaries are increasingly deploying drones guided by physical fiber-optic tethers, which are completely immune to RF jamming and spoofing.⁶ Alternatively, they are integrating autonomous terminal guidance driven by machine vision and artificial intelligence, allowing the drone to navigate to the target using terrain recognition and optical matching even when GPS and C2 links are severed.¹²

If the DoD fields thousands of attritable drones relying on standard RF communications and GPS navigation, they will be swiftly neutralized by peer adversary EW complexes. To survive, U.S. systems must be designed from inception with cognitive EW capabilities—AI-driven radios capable of sensing spectrum interference and seamlessly hopping frequencies, or transitioning to alternative navigation aids when the primary spectrum is denied.⁴²

5. Overlooked Adversary Countermeasures: Directed Energy Weapons

Perhaps the most significant overlooked threat to U.S. drone deployments is the adversary’s rapid advancement and operationalization of Directed Energy Weapons (DEW). For decades, DEWs were viewed as experimental technology relegated to laboratories and controlled test ranges. Today, driven by the urgent need to counter the precise mass of drone swarms, these systems are transitioning into deployable, operational assets on the battlefield.⁴⁰

Both China and Russia are actively fielding DEW systems designed specifically for the counter-UAS (C-UAS) mission, recognizing that directed energy is the only defense capable of inverting the unsustainable cost-exchange ratio of drone warfare.⁶ These weapons fall primarily into two functional categories: High-Energy Lasers and High-Power Microwaves.

High-Energy Lasers (HEL)

HEL systems utilize focused light to physically burn through the optical sensors, control surfaces, or battery compartments of an incoming drone. By delivering destructive energy at the speed of light, lasers eliminate the need for complex target-leading calculations required by kinetic anti-aircraft artillery. Furthermore, with a stable power source, lasers offer an effectively infinite magazine, allowing for continuous engagement without the logistical burden of physical reloading.⁵⁰

China has prioritized the development of HEL systems to protect critical infrastructure and ground forces. They have demonstrated multiple ground-based and vehicle-mounted laser systems, such as the Silent Hunter, which boasts a power output of 30 to 100 kilowatts. This is sufficient to destroy the structural components of small to medium uncrewed aerial vehicles at ranges of up to 4 kilometers.⁴⁹ Russia has similarly deployed the Peresvet system, a ground-based laser complex specifically designed to blind the electro-optical sensors of surveillance satellites and shoot down tactical UAVs.⁴⁹ While lasers are susceptible to atmospheric attenuation and thermal blooming, they remain a highly lethal, low-cost-per-shot countermeasure against unshielded drones.⁵⁰

High-Power Microwaves (HPM)

While lasers must track and dwell on a single target to destroy it, HPM systems emit a wide cone of electromagnetic energy designed to instantaneously disrupt or permanently destroy the unshielded microelectronics within a drone.⁶ HPM is widely considered the ultimate counter-swarm weapon, as a single pulse can simultaneously disable dozens of drones flying in tight formation without requiring precise individual tracking.⁵¹

Chinese defense contractors are aggressively advancing HPM technology for deployable use. Systems such as NORINCO’s Hurricane-3000 are designed to create localized zones of electromagnetic denial, providing close-in protection against multi-axis swarm attacks.⁵² Because HPM systems induce catastrophic voltage spikes in flight controllers and navigation modules, they bypass the aerodynamic and kinetic evasive maneuvers that adversary drones might employ to dodge traditional interceptors.

Implications for U.S. Drone Design

The proliferation of adversary DEWs necessitates a complete reevaluation of U.S. drone design and employment doctrine. The smaller, attritable drones currently planned for mass deployment typically lack the size, weight, and power (SWaP) capacity to carry adequate defenses against directed energy. Installing thermal shielding, reflective coatings, or Bragg mirrors to mitigate laser damage significantly increases the weight and manufacturing complexity of the drone.⁴⁹ Similarly, hardening internal electronics with Faraday cages to survive HPM attacks drives up the cost per unit, eroding the core advantage of attritable mass.⁵³

If the DoD fixates solely on producing millions of unprotected, unshielded drones, it risks fielding a massive force that can be efficiently neutralized by a handful of strategically placed adversary directed-energy batteries. The systemic requirement is therefore to develop swarming tactics that incorporate deception, mass dispersal, and active countermeasures—such as integrating sacrificial decoy drones or deploying laser-jamming payload modules within the swarm to confuse enemy DEW tracking systems before the drones enter lethal range.⁴⁹

6. Systemic Vulnerabilities in U.S. Drone Operations

A persistent vulnerability in U.S. defense planning is the cultural tendency to fixate on the end-product—the drone platform itself—while neglecting the vast, complex systemic architecture required to sustain it in a high-intensity conflict. The United States has a history of optimizing acquisition for exquisite, highly survivable systems, a model that breaks down entirely when confronted with the necessity of fielding thousands of expendable drones.⁵⁴ An effective uncrewed capability is not merely an airframe; it is an integrated ecosystem comprising raw material supply chains, spectrum management protocols, maintenance logistics, and decentralized data infrastructure.

The Material Supply Chain Chokepoint

The DoD’s ambition to field tens of thousands of attritable autonomous systems is severely constrained by an industrial base that is fragmented, expensive, and deeply entangled with adversary-controlled supply chains.⁵⁴ The true vulnerability of the U.S. drone program is found not in software algorithms, but in the domains of metallurgy and chemistry.

The “drone supply chain war” revolves around access to specialized composites, alloys, and semiconductors necessary for mass production. Nearly every modern drone relies on carbon fiber reinforced polymers for the airframe, lithium-ion cells for high-density power storage, and neodymium-iron-boron magnets to convert electrical current into torque for propulsion motors.⁵⁶ China maintains a near-monopoly on the processing and refinement of these critical materials. Currently, China processes roughly two-thirds of the world’s lithium, over 70 percent of graphite anode material, and approximately 90 percent of global sintered-magnet output.⁵⁶ Furthermore, the advanced sensors and AI-processing edge computers required for autonomous flight depend on specialty semiconductors, such as gallium-nitride (GaN) power amplifiers and infrared detectors, whose production is bottlenecked in a limited number of facilities.⁵⁶

If the DoD treats drones as true consumables—expecting high attrition rates and demanding rapid replenishment—a single export restriction from Beijing on rare-earth magnets or graphite could paralyze U.S. production lines within weeks.⁵⁶ True organizational agility requires upstream strategic stockpiling of raw materials, rather than just finished weapons. To secure production, the DoD must rapidly establish redundant, allied-shored refining and manufacturing capacities (e.g., through coproduction with partners in Australia, Japan, and Canada) to ensure the industrial base remains operational during a prolonged conflict.⁵⁶

Spectrum Management and Command Link Logistics

Operating a handful of surveillance drones in uncontested airspace via satellite links is a relatively simple communications task. Operating swarms of thousands of collaborative, autonomous drones in a dense, highly contested electromagnetic environment represents a monumental systemic hurdle.⁴⁷

Drones require spectrum to communicate with operators, share targeting data among the swarm, and relay high-resolution intelligence. In a peer conflict, the electromagnetic spectrum will be severely degraded by adversary jamming, and any active RF emission from a U.S. drone or ground control station will serve as a highly visible beacon for adversary anti-radiation missiles and counter-battery artillery fire.⁵ The traditional model of relying on continuous reach-back to central command posts via persistent data links is a fatal vulnerability.

The systemic requirement is the development of dynamic spectrum management and decentralized data architectures. Drones must be capable of processing intelligence at the tactical edge, sharing only minimal, highly compressed burst transmissions via localized, low-probability-of-intercept mesh networks.⁴ The U.S. military must shift its operational philosophy from ensuring perfect, continuous connectivity to ensuring that systems can execute complex commander’s intents autonomously when communications are completely severed.⁵⁸

Maintenance, Logistics, and Attritable Fleet Management

The deployment of massive drone fleets introduces entirely new logistical burdens that tactical units are currently unequipped to handle. While attritable drones are intended to be low-cost and expendable, they still require significant support infrastructure, including secure storage, transportation, high-capacity battery charging stations, firmware update terminals, and pre-flight diagnostic tools.⁶⁰

The failure rate of commercial-grade and attritable UAS is significantly higher than that of crewed aircraft, requiring a continuous pipeline of spare parts—propellers, motors, optical modules, and communication relays.⁶² U.S. tactical units currently lack the specialized training and equipment necessary to manage the lifecycle of hundreds of autonomous systems in austere, forward-deployed environments.⁶⁴

Building organizational agility requires completely redesigning sustainment. The DoD must mandate Modular Open Systems Architectures (MOSA) across all drone procurement. Open architectures ensure that components are plug-and-play across different drone variants, allowing soldiers to cannibalize damaged systems to repair others without requiring proprietary contractor support or waiting for highly specific replacement parts to travel across vulnerable trans-oceanic supply lines.³

7. Building Organizational Agility and Decision Dominance

To successfully employ drone technology long-term and survive in a rapidly adapting threat landscape, the DoD must fundamentally restructure how it designs, procures, and updates uncrewed systems. The objective is not merely to construct a more technologically advanced drone, but to build an organizational machine capable of learning, iterating, and evolving faster than the adversary.² This requires a paradigm shift across acquisition, tactical fabrication, and artificial intelligence integration.

Rethinking Acquisition: From Platforms to Capabilities as a Service

The traditional acquisition models, such as JCIDS, mandate years of requirements generation, testing, and evaluation, ultimately resulting in highly integrated, proprietary platforms that are exceedingly difficult to upgrade once fielded.¹³ By the time a rigid platform navigates the bureaucratic pipeline and reaches the warfighter, the adversary has already witnessed its prototypes, mapped its signatures, and deployed targeted countermeasures.¹⁴

To outpace this cycle, DoD leadership must transition toward a model of purchasing “Capabilities as a Service” and utilizing rapid, iterative pilot programs.⁸ Rather than locking the military into a decade-long contract for a specific airframe, the DoD should procure modular systems governed by open digital architectures. This software-defined approach allows the military to continuously swap out payloads, radios, and optical sensors from various commercial vendors as the threat environment changes, breaking vendor lock and accelerating deployment.³

The U.S. Navy’s Task Force 59 provides a successful blueprint for this necessary agility. By integrating commercial uncrewed surface vessels with artificial intelligence and mesh networks, and utilizing a workforce comprising reservists and tech industry experts, Task Force 59 demonstrated the ability to rapidly iterate capabilities directly in the operational environment of the Middle East, bypassing traditional bureaucratic chokepoints and fielding viable systems in months rather than years.⁶⁶

Fostering Innovation and Fabrication at the Tactical Edge

The most critical adaptations in drone warfare do not originate in pristine defense laboratories; they are born in trenches and forward operating bases out of operational necessity. Ukrainian forces achieve rapid adaptation precisely because the end-users (the soldiers) are directly integrated with the engineers modifying the software and hardware.⁸ When a new Russian EW frequency is encountered, Ukrainian teams write software patches, 3D-print modified antenna housings, and deploy the updated drone the following day.⁸

The DoD must build an infrastructure that supports this bottom-up innovation, applying the principles of the lean startup model directly to tactical units.⁸ Soldiers must be granted the authority, budget, and tools to modify systems in the field without awaiting top-down approval. Initiatives such as “Fabrication at the Tactical Edge” (FATE)—which involves equipping forward units with ruggedized 3D printers, software coding terminals, and modular COTS components—allow operators to invent, manufacture, and test physical drone modifications and payload adaptations within hours of encountering a new enemy countermeasure.⁶⁹

Furthermore, leadership must cultivate a culture that tolerates acceptable failure at the tactical level. Innovation requires iteration. If a squad attempts a new drone modification and it fails, the organization must rapidly capture that telemetry data, share it across the network, and facilitate a second attempt, rather than subjecting the failure to punitive bureaucratic reviews that stifle future experimentation.²

Implementing Agentic AI for Decision Dominance

As the volume of drones on the battlefield scales into the thousands, human operators will be fundamentally incapable of processing the sheer influx of sensor data, threat warnings, and EW anomalies. Managing this complexity requires a transition from basic automation to “Agentic AI” to achieve true decision dominance.⁹

Unlike traditional AI—which functions primarily as an analytical tool, a predictive model, or a generative summarizer—Agentic AI acts as an autonomous, goal-oriented entity embedded within the command-and-control workflow.⁹ In a drone swarm context, Agentic AI does not simply alert a human commander that the swarm is being jammed. Instead, it actively senses the EW interference, reasons through alternative navigation options, dynamically re-routes the unaffected drones to form a new mesh network, assigns specific drones to act as sacrificial decoys, and generates an optimized course of action for the commander to approve—all in milliseconds.⁹

To realize this, the DoD must invest heavily in the software infrastructure required to host Agentic AI at the edge. The true competitive advantage in uncrewed warfare lies in the sophisticated algorithms that govern collaborative swarm behavior, automated target recognition, and dynamic spectrum evasion, rather than the aerodynamic efficiency or kinetic payload of the drone hardware itself.⁹

8. Strategic Recommendations for DoD Leadership

The Department of Defense is entering an era of precise mass, where low-cost, highly intelligent systems will increasingly dominate the multidomain battlespace.² To ensure long-term viability, maintain operational overmatch, and survive against the rapid adaptation of peer adversaries, DoD leadership must operationalize the following strategic imperatives:

The nation that first masters the systemic integration of uncrewed systems—securing the underlying supply chain, fielding deeply integrated non-kinetic defenses, and weaponizing the learning cycle to adapt faster than the enemy—will dictate the terms of future military competition.¹ Drones are not the terminal end-state of military innovation; they are the catalyst for an entirely new organizational paradigm of warfare. The DoD must look beyond the platform to build an agile, software-defined, and deeply resilient defense enterprise.

Please share the link on Facebook, Forums, with colleagues, etc. Your support is much appreciated and if you have any feedback, please email us in**@*********ps.com. If you’d like to request a report or order a reprint, please click here for the corresponding page to open in new tab.

Sources Used

1. The Quick and the Dead: How Adaptation in Contact Drives Military Advantage, accessed April 24, 2026, https://www.hudson.org/defense-strategy/quick-dead-how-adaptation-contact-drives-military-advantage-bryan-clark-dan-patt-ian-crone
2. The requirement that a force must adapt while it is in combat is built into the inherent nature of war. – SCSP, accessed April 24, 2026, https://www.scsp.ai/wp-content/uploads/2025/11/Adaptation_War_SCSP.pdf
3. The Drone War’s Real Problem Isn’t Technology — It’s Speed, accessed April 24, 2026, https://www.thecipherbrief.com/drone-wars
4. How are Drones Changing Modern Warfare? | Australian Army Research Centre (AARC), accessed April 24, 2026, https://researchcentre.army.gov.au/library/land-power-forum/how-are-drones-changing-modern-warfare
5. PRC Concepts for UAV Swarms in Future Warfare | CNA.org., accessed April 24, 2026, https://www.cna.org/reports/2025/07/PRC-Concepts-for-UAV-Swarms-in-Future-Warfare.pdf
6. Counter-UAS Mission Seen as Killer App for Directed Energy – National Defense Magazine, accessed April 24, 2026, https://www.nationaldefensemagazine.org/articles/2026/1/20/counterdrone-mission-seen-as-killer-app-for-directed-energy
7. Fact Sheet: DoD Strategy for Countering Unmanned Systems – Department of War, accessed April 24, 2026, https://media.defense.gov/2024/Dec/05/2003599149/-1/-1/0/FACT-SHEET-STRATEGY-FOR-COUNTERING-UNMANNED-SYSTEMS.PDF
8. Want Drone Dominance? Let the Squad Fail – Modern War Institute -, accessed April 24, 2026, https://mwi.westpoint.edu/want-drone-dominance-let-the-squad-fail/
9. Decision Dominance in the Age of Agentic AI – Small Wars Journal, accessed April 24, 2026, https://smallwarsjournal.com/2025/10/03/agentic-ai-decision-dominance/
10. Electronic Warfare vs. Directed Energy: Which C-UAS Approach Wins the Scaling War?, accessed April 24, 2026, https://www.dronesense.ai/electronic-warfare-vs-directed-energy-which-c-uas-approach-wins-the-scaling-war-2/
11. How Russia’s Electronic Warfare Blinded Ukrainian Drones — and How Ukraine Fought Back | Military Machine, accessed April 24, 2026, https://militarymachine.com/russia-electronic-warfare-ukraine-drones
12. Drone Warfare in Ukraine: From Myths to Operational Reality – Part 1, accessed April 24, 2026, https://researchcentre.army.gov.au/library/land-power-forum/drone-warfare-ukraine-myths-operational-reality-part-1
13. Why the Army Needs Units Driving Drone Development and How to Do It, accessed April 24, 2026, https://www.armyupress.army.mil/journals/military-review/online-exclusive/2025-ole/drone-development/
14. Achieving Adaptable Scale: Fielding Military Capabilities as a Service | Hudson Institute, accessed April 24, 2026, https://www.hudson.org/events/achieving-adaptable-scale-fielding-military-capabilities-service
15. Adversary Entente Task Force Update, July 9, 2025 | ISW, accessed April 24, 2026, https://understandingwar.org/research/adversary-entente/adversary-entente-task-force-update-july-9-2025/
16. Russian Drone Innovations are Likely Achieving Effects of Battlefield Air Interdiction in Ukraine – Institute for the Study of War, accessed April 24, 2026, https://understandingwar.org/research/russia-ukraine/russian-drone-innovations-are-likely-achieving-effects-of-battlefield-air-interdiction-in-ukraine/
17. Collaboration for a Price: Russian Military-Technical Cooperation with China, Iran, and North Korea – CSIS, accessed April 24, 2026, https://www.csis.org/analysis/collaboration-price-russian-military-technical-cooperation-china-iran-and-north-korea
18. Countering Iran’s UAS swarms ‘requires compressing the kill chain’ – RUSI, accessed April 24, 2026, https://www.rusi.org/news-and-comment/in-the-news/countering-irans-uas-swarms-requires-compressing-kill-chain
19. What Are Iranian Shahed Drones Capable of? From Ukraine to Dubai and US Bases in the Gulf – UNITED24 Media, accessed April 24, 2026, https://united24media.com/war-in-ukraine/what-are-iranian-shahed-drones-capable-of-from-ukraine-to-dubai-and-us-bases-in-the-gulf-16497
20. The new economics of warfare – European Policy Centre (EPC), accessed April 24, 2026, https://www.epc.eu/publication/the-new-economics-of-warfare/
21. Monthly Drone Report – March 2026 – SOF News, accessed April 24, 2026, https://sof.news/drones/march-2026/
22. Iran–U.S. RQ-170 incident – Wikipedia, accessed April 24, 2026, https://en.wikipedia.org/wiki/Iran%E2%80%93U.S._RQ-170_incident
23. Iran claims it decoded all data from captured CIA drone – The Times of Israel, accessed April 24, 2026, https://www.timesofisrael.com/iran-claims-it-decoded-all-data-from-captured-cia-drone/
24. Captured US Stealth Drone, Reversed-Engineered By Iran, Could Help Russia In Gaining Air Superiority Over Ukraine – EurAsian Times, accessed April 24, 2026, https://www.eurasiantimes.com/us-stealth-drone-reversed-engineered-by-iran-to-help/
25. Iran gives Russia copy of US ScanEagle drone as proof of mass production – The Guardian, accessed April 24, 2026, https://www.theguardian.com/world/2013/oct/21/iran-russia-us-scaneagle-spy-drone-production-capture
26. China Takes Control of Indian In-Service Drone: A Wake-Up Call for Indigenous Development – AlphaDefense.in, accessed April 24, 2026, https://alphadefense.in/index.php/2025/03/25/china-control-indian-drone-security-flaw/
27. The PLA’s Unmanned Aerial Systems – Air University, accessed April 24, 2026, https://www.airuniversity.af.edu/Portals/10/CASI/documents/Research/PLAAF/2018-08-29%20PLAs_Unmanned_Aerial_Systems.pdf
28. China’s Military Unmanned Aerial Vehicle Industry, accessed April 24, 2026, https://www.uscc.gov/sites/default/files/Research/China’s%20Military%20UAV%20Industry_14%20June%202013.pdf
29. Russia’s Iranian-Made UAVs: A Technical Profile | Royal United Services Institute – RUSI, accessed April 24, 2026, https://www.rusi.org/explore-our-research/publications/commentary/russias-iranian-made-uavs-technical-profile
30. A U.S. Spy Drone Reverse-Engineered By Both Russia & China: The Untold Story Of Lockheed Martin’s D-21 – EurAsian Times, accessed April 24, 2026, https://www.eurasiantimes.com/u-s-spy-drone-reverse-engineered-by-both-russia-china-the-untold-story-of-lockheed-martins-3-d-21/
31. Uncovering an Adversary UAS Unit and Shadow Pipeline – DroneSec, accessed April 24, 2026, https://dronesec.com/resources/7643d3e0-3358-11f1-ad69-c3
32. How the U.S. Army ‘Replicates’ Enemy Drones to Destroy Them – The National Interest, accessed April 24, 2026, https://nationalinterest.org/blog/buzz/how-us-army-replicates-enemy-drones-destroy-them-79601
33. Security analysis of drones systems: Attacks, limitations, and recommendations – PMC – NIH, accessed April 24, 2026, https://pmc.ncbi.nlm.nih.gov/articles/PMC7206421/
34. UAV Exploitation: A New Domain for Cyber Power | CCDCOE, accessed April 24, 2026, https://ccdcoe.org/uploads/2018/10/Art-14-Assessing-the-Impact-of-Aviation-Security-on-Cyber-Power.pdf
35. Cyber threat in drone systems: bridging real-time security, legal admissibility, and digital forensic solution readiness – Frontiers, accessed April 24, 2026, https://www.frontiersin.org/journals/communications-and-networks/articles/10.3389/frcmn.2025.1661928/full
36. A Comprehensive Approach to Countering Unmanned Aircraft Systems – Joint Air Power Competence Centre, accessed April 24, 2026, https://www.japcc.org/wp-content/uploads/A-Comprehensive-Approach-to-Countering-Unmanned-Aircraft-Systems.pdf
37. The Vulnerability Management Race Is Over. It’s Time to Focus on Exposure., accessed April 24, 2026, https://securityboulevard.com/2026/04/the-vulnerability-management-race-is-over-its-time-to-focus-on-exposure/
38. GOVERNMENT PERSPECTIVE: Directed Energy in Air Base Defense Can Save the Arsenal, accessed April 24, 2026, https://www.nationaldefensemagazine.org/articles/2025/8/11/government-perspective-directed-energy-in-air-base-defense-can-save-the-arsenal
39. Electronic warfare – Wikipedia, accessed April 24, 2026, https://en.wikipedia.org/wiki/Electronic_warfare
40. Visions for the next 40 years of U.S. Department of Defense Directed Energy technologies – Air Force Research Laboratory, accessed April 24, 2026, https://www.afrl.af.mil/Portals/90/Documents/RD/Directed_Energy_Futures_2060_Final29June21_with_clearance_number.pdf
41. Lessons from the War in Ukraine for Space: Challenges and Opportunities for Future Conflicts – RAND, accessed April 24, 2026, https://www.rand.org/content/dam/rand/pubs/research_reports/RRA2900/RRA2950-1/RAND_RRA2950-1.pdf
42. Drone Swarm Navigation in GNSS-Challenged and Cluttered Environments – Medium, accessed April 24, 2026, https://medium.com/@gwrx2005/drone-swarm-navigation-in-gnss-challenged-and-cluttered-environments-d50388bc31b3
43. COUNTERING THE SWARM – Amazon S3, accessed April 24, 2026, https://s3.us-east-1.amazonaws.com/files.cnas.org/documents/Report_CUAS_Defense_Sep-2025_final.pdf
44. Electronic Warfare: Mastering the Invisible Battlefield | by S-Ag3 – Medium, accessed April 24, 2026, https://medium.com/@ingartsq2/electronic-warfare-mastering-the-invisible-battlefield-191b0b92d3ef
45. Russian Force Generation & Technological Adaptations Update, October 9, 2025, accessed April 24, 2026, https://understandingwar.org/research/russia-ukraine/russian-force-generation-technological-adaptations-update-october-9-2025/
46. Exploiting Offensive Use of Small Unmanned Aerial Systems (sUAS): Learning from Our Adversaries > Air University (AU) > Wild Blue Yonder, accessed April 24, 2026, https://www.airuniversity.af.edu/Wild-Blue-Yonder/Articles/Article-Display/Article/3836715/exploiting-offensive-use-of-small-unmanned-aerial-systems-suas-learning-from-ou/
47. UAV swarm algorithm boosts spectrum resilience in contested airspace – SpaceWar.com, accessed April 24, 2026, https://www.spacewar.com/reports/UAV_swarm_algorithm_boosts_spectrum_resilience_in_contested_airspace_999.html
48. Directed Energy Weapons Market Report, Industry and Market Size & Revenue, Share, Forecast 2024–2030, accessed April 24, 2026, https://www.strategicmarketresearch.com/market-report/directed-energy-weapons-market
49. Counter Directed Energy Weapons and the Defense of Naval Unmanned Aerial Vehicles, accessed April 24, 2026, https://nps.edu/documents/10180/142489929/JDE_7-2_Johnson.pdf
50. War at the speed of light: the emerging role of directed-energy weapons | The Strategist, accessed April 24, 2026, https://www.aspistrategist.org.au/war-at-the-speed-of-light-the-emerging-role-of-directed-energy-weapons/
51. Countering the Swarm – CNAS, accessed April 24, 2026, https://www.cnas.org/publications/reports/countering-the-swarm
52. China Pushes Forward with UAV and Counter-UAV Technology – Datenna, accessed April 24, 2026, https://www.datenna.com/resources/china-pushes-forward-with-uav-and-counter-uav-technology/
53. Ghosts of the Road: What the Failed War on IEDs Means for Drones, accessed April 24, 2026, https://warontherocks.com/ghosts-of-the-road-what-the-failed-war-on-ieds-means-for-drones/
54. The Drone Gap: Why the U.S. Industrial Base Continues to Fall …, accessed April 24, 2026, https://www.icitech.org/post/the-drone-gap-why-the-u-s-industrial-base-continues-to-fall-behind-in-a-world-at-war-by-drone
55. Swarms over the Strait – CNAS, accessed April 24, 2026, https://www.cnas.org/publications/reports/swarms-over-the-strait
56. The Drone Supply Chain War: Identifying the Chokepoints to Making …, accessed April 24, 2026, https://www.csis.org/analysis/drone-supply-chain-war-identifying-chokepoints-making-drone
57. Spectrum Contested Environments – Marine Corps Association, accessed April 24, 2026, https://www.mca-marines.org/gazette/spectrum-contested-environments/
58. How Communications Technology Enables DoW’s Drone Scaling – Elsight, accessed April 24, 2026, https://www.elsight.com/blog/how-communications-technology-enables-dows-drone-scaling/
59. AGILE COMBAT EMPLOYMENT – USAF, accessed April 24, 2026, https://www.af.mil/Portals/1/documents/Force%20Management/AFDN_1-21_ACE.pdf
60. Operating Low-Cost, Reusable Unmanned Aerial Vehicles in Contested Environments – RAND, accessed April 24, 2026, https://www.rand.org/content/dam/rand/pubs/research_reports/RR4400/RR4407/RAND_RR4407.pdf
61. Why is Maintenance Important for Large Drone Fleets’ Management? | AirHub, accessed April 24, 2026, https://www.airhub.app/resources/news/large-drone-fleets-maintenance
62. (PDF) Maintenance of a drone fleet – ResearchGate, accessed April 24, 2026, https://www.researchgate.net/publication/329690289_Maintenance_of_a_drone_fleet
63. Maintenance of a Drone Fleet – BQR, accessed April 24, 2026, https://www.bqr.com/post/maintenance-of-a-drone-fleet
64. Small Uncrewed Aircraft Systems in Divisional Brigades: Requirements and Findings – RAND, accessed April 24, 2026, https://www.rand.org/content/dam/rand/pubs/research_reports/RRA2600/RRA2642-1/RAND_RRA2642-1.pdf
65. KSE_Institute_Report_Harnessin, accessed April 24, 2026, https://kse.ua/wp-content/uploads/2025/11/KSE_Institute_Report_Harnessing_Ukraines_Drone_Innovations_to_Advance.pdf
66. Integrating USVs & AI into an Operational Maritime Environment – Saildrone, accessed April 24, 2026, https://www.saildrone.com/missions/task-force-59-unmanned-integration
67. Task Force 59: The future of the Navy’s unmanned systems or a one-off win?, accessed April 24, 2026, https://defensescoop.com/2022/02/08/task-force-59-the-future-of-the-navys-unmanned-systems-or-a-one-off-win/
68. How are Drones Changing War? The Future of the Battlefield – CEPA, accessed April 24, 2026, https://cepa.org/article/how-are-drones-changing-war-the-future-of-the-battlefield/
69. Fabrication at the Tactical Edge – National Defense University, accessed April 24, 2026, https://www.ndu.edu/News/Article-View/Article/4445402/fabrication-at-the-tactical-edge/
70. Beyond Linear Planning – Marine Corps University, accessed April 24, 2026, https://www.usmcu.edu/Outreach/Marine-Corps-University-Press/MCU-Journal/JAMS-vol-16-no-2/Beyond-Linear-Planning/
71. Air Force Future Operating Concept, accessed April 24, 2026, https://www.af.mil/Portals/1/images/airpower/AFFOC.pdf
72. Adapting to Win | Hudson Institute, accessed April 24, 2026, https://www.hudson.org/defense-strategy/adapting-win-us-navy-rapid-capabilities-office-nrco-new-approach-military-acquisition-bryan-clark-dan-patt
73. Data Poisoning as a Covert Weapon: Securing U.S. Military Superiority in AI-Driven Warfare, accessed April 24, 2026, https://lieber.westpoint.edu/data-poisoning-covert-weapon-securing-us-military-superiority-ai-driven-warfare/

1. Executive Summary

The United States Department of Defense (DoD) is actively pursuing a fundamental transformation in its force structure, transitioning from a reliance on exquisite, manned, high-cost platforms toward the mass deployment of small, attritable, autonomous systems. Initiatives such as the Replicator program mandate the fielding of thousands of these systems across multiple domains within an aggressive 18-to-24-month timeline.¹ This strategic pivot is largely a response to the “intelligentization” of competitor forces, specifically the People’s Liberation Army (PLA), which aims to leverage artificial intelligence (AI) and advanced technologies to offset traditional U.S. conventional advantages.³ However, an over-fixation on the physical hardware—airframes, propulsion, and payload—has obscured a far more complex systemic bottleneck: the algorithmic architecture required to ensure these systems operate legally, ethically, and safely in contested environments.

Designing and manufacturing a drone is a largely solved engineering problem. Encoding the Law of Armed Conflict (LOAC) and mission-specific Rules of Engagement (ROE) into a machine-learning algorithm is not.⁵ The current strategic posture risks fielding capabilities that possess high degrees of kinetic lethality but lack the deterministic boundaries required to comply with international humanitarian law (IHL) and prevent unintended escalation. The operational reality is that autonomous systems can respond to threats faster than a human military force can perceive, orient, decide, and act, which drives the immense pressure for their rapid deployment.⁷ Yet, without deliberate systemic safeguards, this acceleration introduces unprecedented risks to strategic stability.

This report provides a detailed analysis of the legal, technical, and operational hurdles inherent in deploying autonomous weapon systems (AWS). It examines the friction between the probabilistic nature of modern AI and the rigid, deterministic requirements of military law.⁸ It evaluates the necessity of shifting legal oversight directly into the software design phase ⁹, the continuous nature of algorithmic testing and evaluation (T&E) ¹⁰, and the severe risks of crisis instability when autonomous systems interact at machine speeds.¹¹ Finally, it outlines the specific policy adaptations and oversight structures leadership must mandate to responsibly govern human-machine teaming (HMT) and lethal autonomy, moving beyond abstract ethical principles toward executable engineering standards.¹²

2. The Strategic Context and the Hardware Fallacy

The strategic imperative driving the integration of autonomous systems is clear: competitors are heavily investing in AI and autonomous swarm technologies to offset traditional U.S. advantages.² To counter adversarial advantages in mass, particularly the anti-access/area-denial (A2/AD) capabilities deployed in the Indo-Pacific, the DoD has prioritized the rapid development of All-Domain Attritable Autonomous (ADA2) systems.¹

2.1. The Replicator Initiative and the Demand for Mass

Launched by the Deputy Secretary of Defense, the Replicator initiative seeks to catalyze progress in a military innovation cycle that has historically been too slow, shifting the focus to platforms that are “small, smart, cheap, and many”.¹ The first iteration, Replicator 1, focuses on fielding thousands of uncrewed systems across aerial, ground, maritime, and space domains, selecting systems like AeroVironment’s Switchblade 600, Anduril’s Altius-600 and Ghost-X, and Performance Drone Works’ C-100.¹⁵ The subsequent phase, Replicator 2, targets counter-small unmanned aerial systems (C-sUAS), drawing heavily on operational lessons from contemporary battlefields such as the conflict in Ukraine.¹⁵

Despite these clear programmatic goals, public and institutional discourse often defaults to a hardware-centric paradigm. Strategic planners and acquisition professionals frequently focus on range, payload capacity, unit cost, and aerodynamic performance. This approach overlooks the reality that an advanced autonomous system is primarily a software platform housed within a physical shell. The true measure of a system’s combat readiness is not its mechanical reliability, but the maturity of its computer vision models, the resilience of its data fusion algorithms against electromagnetic interference, and the operational integrity of its targeting logic.¹⁶

2.2. The Shift to Algorithmic Warfare

When autonomous systems are deployed to execute complex missions in denied electromagnetic environments without continuous communication links, the software becomes the sole arbiter of lethal force.² If the system’s foundational models have not been rigorously trained to distinguish between a functional anti-aircraft battery and a destroyed civilian vehicle resembling one, the hardware’s kinetic capabilities are irrelevant; the deployment becomes an immediate legal liability and a strategic risk.¹⁸

Advances in military applications of AI further strengthen the convergence between the cyber domain of operations (digital code) and the electromagnetic environment (electrons).¹⁶ In a crowded and contested spectrum, the distinction between a conventional kinetic attack and a cyber-attack blurs. Adversaries can target model weights through espionage, poison training datasets, spoof sensors on intelligence, surveillance, and reconnaissance (ISR) platforms, or disable data relays.¹⁶ The systemic requirement, therefore, is not merely to build a drone, but to construct an entire software assurance lifecycle that moves at the speed of code, rather than the traditional, multi-year acquisition cycles designed for aircraft carriers and fighter jets.¹⁰

3. The Legal and Ethical Mandates Governing Autonomy

The deployment of autonomous and semi-autonomous systems is governed by a strict, evolving framework of international and domestic directives. Leadership must recognize that algorithmic weapon systems do not exist in a legal vacuum; they must navigate the same complex web of international treaties, customary law, and domestic policy that governs human warfighters.

3.1. DoD Directive 3000.09 and Definitional Clarity

The foundational document within the DoD is(https://www.esd.whs.mil/portals/54/documents/dd/issuances/dodd/300009p.pdf), which was significantly updated in January 2023 to address the rapid advancements in AI.¹² The directive establishes that all autonomous and semi-autonomous weapon systems must be designed to allow commanders and operators to exercise “appropriate levels of human judgment over the use of force”.¹³

A critical element of this directive is its definitional precision. It differentiates between semi-autonomous systems—which engage specific targets or specific target groups that have been selected by a human operator (e.g., lock-on-after-launch or “fire and forget” munitions)—and fully autonomous weapon systems, which, once activated, can select and engage targets without further human intervention.¹³ The directive also mandates that the integration of AI capabilities must align with the DoD’s Responsible AI (RAI) Ethical Principles, which dictate that systems must be responsible, equitable, traceable, reliable, and governable.²² Systems must be subjected to rigorous verification and validation (V&V) before deployment to minimize the probability and consequences of failures that could lead to unintended engagements.¹²

3.2. Integration of the Law of Armed Conflict (LOAC)

Beyond domestic directives, any weapon system deployed by U.S. forces must comply with the core tenets of the LOAC, which is heavily rooted in the 1949 Geneva Conventions and their 1977 Additional Protocols.¹⁴ The legality of AWS under IHL ultimately hinges on their capacity to adhere to these foundational principles:

• Distinction: The absolute requirement to differentiate between lawful military objectives (combatants and military equipment) and protected civilian persons or objects.¹⁴
• Proportionality: The requirement that the anticipated civilian harm or collateral damage must not be excessive in relation to the concrete and direct military advantage anticipated from the attack.¹⁴
• Precaution: The obligation to take all feasible measures in the planning and execution of an attack to avoid, or minimize, civilian harm.¹⁴
• The Martens Clause: A fallback principle stating that in cases not covered by international agreements, civilians and combatants remain under the protection of the principles of humanity and the dictates of public conscience.¹⁴

While semi-autonomous systems rely on human operators to fulfill these legal obligations prior to launch, fully autonomous systems shift the immense burden of compliance entirely onto the algorithm.⁹

3.3. Historical Precedents and the Accountability Gap

Although the term “autonomous weapons” conjures modern imagery of swarming drones, the underlying legal concept is not entirely novel. Battlefields have long been shaped by autonomous mechanisms like drifting naval mines, torpedoes, and victim-activated landmines designed to strike targets without real-time human input.¹⁴ The 1997 Ottawa Convention prohibits anti-personnel mines precisely because they are inherently indiscriminate; they cannot distinguish between a combatant’s footstep and a child’s.¹⁴ However, anti-vehicle mines remain permitted under specific conditions, highlighting that the international community has historically regulated autonomy based on the capability of the weapon to adhere to the principle of distinction.¹⁴

The modern challenge is that AI-driven AWS are vastly more complex than pressure-plate mines. As algorithms begin to make decisions that determine lethality, they force a re-examination of accountability.¹⁴ If an autonomous system commits an IHL violation, existing criminal liability systems—designed to judge human intent, negligence, and mens rea—are ill-equipped to handle the distribution of responsibility among programmers, procurement officers, and the battlefield commanders who activated the system.¹⁴ This accountability gap deprives victims of justice and undermines the preventive power of international law.¹⁴

4. The Algorithmic Translation of Legal Frameworks

The core technical challenge facing the defense engineering establishment is the translation of abstract, qualitative legal concepts into quantitative, explicit algorithmic logic.⁸ LOAC was drafted by humans, for human interpretation, relying heavily on contextual understanding, reasonable judgment, and situational nuance.⁶ A machine cannot intuitively understand context; it can only execute code.

4.1. The Conflict Between Probabilities and Deterministic Law

Modern machine learning, particularly the deep neural networks utilized for computer vision and autonomous target acquisition, operates fundamentally on statistical probabilities, not deterministic rules.⁸ An algorithm does not possess semantic knowledge that a target is an enemy tank; rather, it calculates a mathematical probability (e.g., 92% confidence) that a specific cluster of pixels within its sensor feed matches the distribution of its training data labeled as “tank”.¹⁹

This probabilistic nature is inherently at odds with strict legal thresholds. If a targeting algorithm operates with an 8% error rate, and that statistical error results in a kinetic strike on a civilian structure, the probabilistic nature of the system offers no legal defense under IHL. Furthermore, while an AI might be trained to recognize the Distinction between a soldier holding a rifle and a civilian holding a rake, the principle of Proportionality requires an incredibly complex value judgment. How does an algorithm assign a numerical, calculable value to abstract concepts like “anticipated military advantage” versus “collateral damage estimation”?⁹ Algorithms are currently incapable of understanding hostile intent from body language, deducing the strategic value of a target in a broader campaign, or recognizing subtle cues of surrender (rendering a target hors de combat).⁹

These deficiencies are amplified in non-international armed conflicts and urban warfare, where combatants frequently operate without uniforms among the civilian population. In such environments, AI models are highly susceptible to pattern-recognition failures, especially if they encounter conditions that differ markedly from their sterile training datasets.¹⁸

4.2. Probabilistic vs. Logic-Based Modeling

To resolve the friction between statistical probabilities and legal boundaries, system developers must look beyond purely statistical machine learning and incorporate formal methods or logic-based modeling. The two dominant machine learning paradigms—imitation learning and reinforcement learning—can produce highly capable systems, but neither inherently preserves the kind of strict constraint satisfaction required by law.²⁶

Table 1: Comparative analysis of modeling approaches for integrating operational logic in military AI.

A hybrid architecture is increasingly recognized as a vital pathway forward. The machine learning model provides the sensory processing and perception, while a logic-based “governor” ensures the output complies with predefined rules.⁸

4.3. Digital Rules of Engagement

Rules of Engagement are a positive statement of intent, underpinned by legal, policy, capability, and operational factors that are specific to a particular theater of operations.²⁸ They provide commanders with control over the implementation of force and provide warfighters with clear guidelines on permissible actions.²⁸

Developing “Algorithmic ROE” involves creating machine-readable constraints that can be adjusted dynamically based on the theater of operations.²⁵ For an autonomous system to be viable, it must be able to accept a digital ROE card that restricts its geographic boundaries, limits its weapon release authority based on positive identification thresholds (e.g., requiring a 95% confidence score for a military vehicle, but a 99% score if human presence is detected), or mandates a hand-off to a human operator if uncertainty crosses a specific threshold.¹⁹

However, current academic and military discourse indicates that even if specific algorithmic ROE cards were created for tactical use, there is no certainty that AWS at their current level of technological development could properly interpret and apply these constraints in chaotic battlefield conditions.²⁵ The translation of ROE into code is not merely a programming task; it is a profound legal translation that requires multidisciplinary oversight.

5. Shifting Legal Oversight Left: The Redefined Role of Judge Advocates

The traditional military acquisition and operational process involves Judge Advocates (JAs) conducting legal reviews of weapon systems after they are developed, usually just prior to fielding or during the operational planning phase. In the context of autonomous AI, this arms-length, post-development review is deeply flawed, outdated, and often legally inadequate.⁹

5.1. The Laboratory as the New Battlefield for LOAC

Because AI algorithms learn from their training data, the goals, parameters, and constraints guiding a learner’s decisions are established in the laboratory, long before a conflict exists.⁹ The design timeframe is the most critical period because it establishes the foundational logic of the system. Spotting LOAC issues at this stage is absolutely necessary.⁹

If an algorithm is trained in a civilian or sterile laboratory environment without specific, coded parameters penalizing the targeting of protected objects or individuals who are hors de combat, the final model will inherently lack that legal distinction.⁹ Relying solely on ad hoc requests for legal support or end-stage weapons reviews ignores how autonomy transforms battlefield LOAC concerns into laboratory LOAC concerns.⁹

5.2. Judge Advocates as Combat Advisors in Design

To address this systemic flaw, leadership must mandate a cultural and procedural shift, transitioning JAs from being mere end-stage “reviewers” to active “combat advisors” embedded directly within software engineering and design teams.⁹

By partnering with data scientists and technologists at entities like Army Futures Command (AFC) or the Defense Innovation Unit (DIU), JAs can spot LOAC issues during the nascent stages of technology development.⁹ They provide critical value during the requirements phase by ensuring that an agency’s official needs adequately capture the necessary parameters for LOAC compliance.⁹

Furthermore, these JA-engineering teams must define explicit “human touchpoints” within the system architecture. They must clearly delineate where an AI is legally permitted to execute autonomously, and where the law dictates that a human presence or intervention is legally or operationally required before lethal force is applied.⁹ This early integration prevents the costly and operationally disastrous reality of engineering an exquisite, multi-million-dollar AI system only to have it barred from deployment due to fundamental LOAC incompatibilities discovered during a final, inflexible legal review.

6. Data Logistics and the Reality of Synthetic Environments

Autonomous systems are fundamentally bound by the quality, variety, structure, and integrity of their training data.⁷ The systemic requirement to build, deploy, and evolve these systems demands massive data logistics, an area where the DoD is currently facing significant friction.

6.1. The Scale of the Data Challenge and Data Masking

The application of computer vision for target acquisition of military combat vehicles requires ample, highly accurate labeled data.²⁹ The scale of this requirement is staggering; the National Geospatial-Intelligence Agency (NGA) is currently prepping a data-labeling effort estimated to cost nearly $800 million, reflecting the immense resources required to annotate images to train machine-learning models.³⁰

However, data aggregation from multiple classified and unclassified sources often results in datasets that are not formatted for immediate use, slowing down the AI modeling process.³¹ A significant hurdle is classification. Army investments in data-masking research are critical; employing software tools that can mask sensitive information allows for datasets to be declassified and used safely in unclassified AI-modeling environments, vastly expanding the pool of available training data.³¹

6.2. Training on the Edge of Reality: Synthetic Data

Acquiring labeled data of adversary combat vehicles in diverse, realistic combat environments (e.g., heavy fog, night operations, dense urban clutter, active electronic warfare) is practically impossible to achieve purely through real-world collection. To bridge this critical gap, the DoD and defense contractors rely heavily on synthetic data generated through tools like Unreal Engine 5, generative AI, and Stable Diffusion.¹⁹

Combining real and synthetic data improves object detection performance significantly.²⁹ However, synthetic environments carry inherent risks. If the synthetic data inadvertently encodes biases, lacks sufficient variance, or fails to accurately represent the complex physical realities of the electromagnetic spectrum (such as infrared signatures and thermal bleed), the model will experience severe performance degradation when transferred to a live combat environment.¹⁹ A model that performs flawlessly in a sanitized simulation may fail catastrophically when confronted with the noisy, chaotic data of a real-world battlefield.

[Insert image of a system architecture diagram illustrating the data pipeline: from raw sensor collection and synthetic data generation, through data masking and labeling, leading to model training and deployment to the tactical edge]

6.3. Tactical Bandwidth and Model Decay

A critical, often overlooked vulnerability of military AI is the bandwidth constraint at the tactical edge.¹⁹ In a denied, degraded, intermittent, or limited (DDIL) environment, maintaining continuous, high-bandwidth communication with forward-deployed autonomous swarms is highly unlikely.

If an adversary introduces a new countermeasure, changes their camouflage techniques, or if the operational environment shifts rapidly (e.g., weather changes affecting sensor fidelity), the deployed AI model may begin to suffer from “model drift”.¹⁹ The algorithm’s accuracy degrades, increasing the risk of false positives, fratricide, or civilian casualties. Because neural network updates are data-heavy, pushing a new, retrained model to a drone mid-flight or deep within a contested zone is technologically challenging.¹⁹

Leadership must recognize that an autonomous system’s legal compliance has an operational expiration date. Without the reliable ability to update models in theater, systems must be programmed with graceful degradation protocols—automatically reducing their level of autonomy, reverting to safer baselines, or returning to base when their internal confidence scores drop below legally permissible thresholds.¹⁰

7. The Lifecycle: Redefining Test and Evaluation (T&E)

The historical DoD paradigm of acquiring software via “block upgrades” every few years is entirely obsolete in the age of algorithmic warfare.³² As adversaries rapidly adapt to U.S. AI behaviors and capabilities, the U.S. military must be prepared to update algorithms in a matter of hours or days, not months or years.³² This reality requires a radical overhaul of the DoD Test and Evaluation (T&E) frameworks.

7.1. Moving from Static Testing to the T&E Continuum

The former director of the Joint Artificial Intelligence Center (JAIC) has noted that the Pentagon is not yet well-postured for the T&E of AI, which requires continuous updating.³² If an AI system is not updated continuously, “it’s going to go stale. It’s not going to work as advertised. The adversary is going to corrupt it, and it’ll be worse than not having AI in the first place”.³²

To address this, the Developmental Test and Evaluation (DT&E) of Autonomous Systems Guidebook establishes that autonomous systems require a “T&E continuum”.¹⁰ Because self-learning systems adapt dynamically to new data and changing environments, a system deemed safe and LOAC-compliant on a Tuesday may exhibit unpredictable, non-compliant behavior by a Thursday.¹⁰ Continuous Testing (CT) replaces rigid, static milestones, relying on iterative testing where models are evaluated and refined as new data emerges.¹⁰

This process requires decomposing the dynamic observe-orient-decide-act (OODA) loop of the algorithm to evaluate exactly how the system perceives its environment, processes information, and makes decisions.¹⁰ The Chief Digital and Artificial Intelligence Office (CDAO) is currently producing best practices and an Assurance Case Framework for Trustworthy AI to guide these exact processes.³³

7.2. Runtime Assurance and Adversarial Testing

To safely field these systems despite their inherent unpredictability, the DoD employs Runtime Assurance (RTA) mechanisms.¹⁰ RTA acts as a separate, highly verified software monitor that runs parallel to the complex AI model in real-time. If the AI proposes an action that violates its safety bounds, geofences, or programmed ROE constraints, the RTA intervenes, overriding the AI and returning the system to a safe, pre-approved baseline state.¹⁰

Furthermore, T&E must heavily involve continuous adversarial testing.¹⁰ Testers must act as the enemy, actively attempting to poison the training datasets, spoof the sensors, exploit algorithmic biases, or introduce chaotic variables.¹⁰ The goal is to identify exploitable vulnerabilities before deployment and ensure that when the system inevitably encounters adversarial interference, it fails safely rather than catastrophically.

8. Command Architecture and Human-Machine Teaming

The deployment of thousands of attritable autonomous systems—the core goal of Replicator—inherently alters the structure of military command and control (C2).³⁵ The traditional paradigm of one human operator remotely piloting one drone (e.g., an MQ-9 Reaper) is mathematically and logistically impossible at the scale currently envisioned.³⁷

8.1. Redefining Human Control and Cognitive Load

To manage mass, operators must transition from being “in the loop” (direct manual control of every action) to “on the loop” (supervisory control), managing entire fleets or swarms of systems simultaneously.³⁵ This constitutes the evolution of Human-Machine Teaming (HMT), which combines human strategic intent, contextual awareness, and moral judgment with the immense processing speed, endurance, and data synthesis of machines.³⁸

However, this transition introduces severe cognitive burdens on the warfighter.³⁵ If a single infantry unit is acting as a controller for up to 250 drones—a scenario explored in DARPA’s OFFSET program—the human operator cannot possibly review the sensor feed of every individual drone prior to a lethal engagement.³⁷

Table 2: The spectrum of human control in autonomous weapon systems, illustrating the inverse relationship between scalability and direct legal liability.

To mitigate cognitive overload, the HMT interface must act as an intelligent filter. It must synthesize the chaotic battlespace, presenting the human operator only with critical anomalies, strategic deviations, or specific requests for engagement authorization that require human contextual judgment.³⁹

8.2. Swarm Dynamics, Emergent Behavior, and Logistics

Autonomous swarms present a unique operational and legal challenge. In a true swarm, individual drones are not necessarily programmed with the entire mission plan, nor are they centrally controlled by a single node. Instead, they operate on decentralized algorithms—similar to flocking behavior in nature—sharing data, adapting to interference independently, and collectively solving problems.⁴⁰ Programs like SATURN aim to provide this resilient, decentralized behavior to heterogeneous swarms of unlimited size.³⁹ The 2016 Perdix drone test, launching over 100 micro-drones from F/A-18s, successfully demonstrated collective decision-making and self-healing swarm behavior.⁴⁰

While highly resilient to communications jamming, decentralized swarms operate through emergent behavior—complex actions that arise from the interaction of the swarm members rather than explicit, top-down programming. Overseeing emergent behavior requires command structures that prioritize strict boundary setting (e.g., absolute geofencing, maximum loiter times, strict target-type restrictions) rather than micro-management, ensuring that the swarm’s collective, emergent action never violates the overarching ROE.⁴¹

Furthermore, these autonomous capabilities are not limited to kinetic strikes. Drone technology is increasingly viewed as a solution for sustainment and logistics operations.⁴² Autonomous swarms can provide continuous monitoring and security for supply convoys and logistics nodes in large-scale combat operations, protecting vulnerable sustainment forces without requiring dedicated, manned security details.⁴²

9. Crisis Stability and the Risk of Unintended Escalation

Perhaps the most severe strategic risk associated with the proliferation of autonomous weapons is their potential to radically undermine crisis stability.⁴³ The integration of AI into military platforms inherently compresses the timeline of decision-making. Operations transition from “human speed”—which allows for pauses, diplomatic intervention, and the assessment of strategic intent—to “machine speed”.⁴⁴

9.1. Algorithmic Flash Wars

If U.S. autonomous swarms encounter adversarial autonomous systems in a contested zone, the interactions and calculations occur in milliseconds.¹¹ Without human pauses to assess intent or de-escalate, there is a profound risk of miscalculation. A routine defensive maneuver by a U.S. drone, executed autonomously to avoid a collision, might be mathematically interpreted by an adversary’s AI as an aggressive, pre-launch attack profile.¹¹

This misperception could trigger an automated counter-attack, generating an immediate, uncontrolled escalation spiral—a phenomenon termed a “flash war”—before human commanders in either nation are even aware an engagement has occurred.⁴⁷ The National Security Commission on AI (NSCAI) explicitly warned that AI-enabled systems reduce the time and space available for de-escalatory measures.¹¹

9.2. Escalation and Strategic Deterrence

The rapid deployment of autonomous capabilities can also inadvertently threaten a competitor’s strategic deterrents, potentially lowering the threshold for the use of weapons of mass destruction (WMD). If an adversary perceives that U.S. autonomous swarms possess the surveillance density and autonomy to locate, track, and strike their second-strike nuclear assets, they may adopt a destabilizing “use it or lose it” posture during a conventional crisis.⁴⁵

To mitigate these severe risks, the DoD must actively consider the implementation of automated “de-escalation routines” within its algorithms. More importantly, the U.S. must support international confidence-building measures (CBMs).⁴³ Unilateral declarations or bilateral agreements to maintain positive human control over nuclear launch decisions, or establishing technical protocols for autonomous systems to broadcast benign intent in peacetime scenarios, are necessary steps to preserve strategic stability.⁴³

10. Required Oversight and Policy Adaptations

Hardware development will consistently outpace the evolution of doctrinal and ethical frameworks unless DoD leadership implements rigid, proactive oversight structures. DoDD 3000.09 provides a foundational starting point, but it requires aggressive enforcement and expansion to address the nuanced realities of algorithmic warfare.¹³

10.1. Strengthening Senior Review Mechanisms

Currently, fully autonomous weapon systems must undergo a rigorous Senior Review by the Under Secretary of Defense for Policy, the Under Secretary for Research and Engineering, and the Vice Chairman of the Joint Chiefs of Staff prior to entering formal development and before fielding.¹² Leadership must ensure these reviews are strictly enforced and are never treated as mere bureaucratic formalities to be waived for the sake of acquisition speed.⁵⁰

The newly established Autonomous Weapon Systems Working Group must be empowered with the technical expertise to halt programs that fail to prove algorithmic interpretability.¹² If an AI model operates as an impenetrable “black box” where developers and commanders cannot adequately explain why the algorithm selected a specific target, it cannot be legally certified for combat operations, regardless of its statistical success rate in simulation.¹⁴

10.2. Closing Policy Loopholes and Standardizing Frameworks

The 2023 iteration of DoDD 3000.09 has faced legitimate critique for applying exclusively to the Department of Defense. This leaves a concerning policy vacuum for autonomous systems utilized by intelligence agencies, such as the CIA, which have historically played an active role in the use of armed drones outside traditional armed conflict environments.²¹ Leadership should advocate for a comprehensive, government-wide policy that standardizes the ethical development and use of lethal autonomy across all federal agencies.

Furthermore, leadership must mandate the use of Algorithmic Impact Assessments prior to deployment.²⁷ These proactive assessments evaluate the potential societal harms, escalation risks, and LOAC vulnerabilities inherent in a system’s training data before it reaches the battlefield. Finally, legal accountability frameworks must be clarified in doctrine. If an autonomous system commits a LOAC violation due to unforeseen model drift, flawed synthetic training data, or unpredictable emergent swarm behavior, the chain of legal accountability—from the battlefield commander who authorized the deployment to the acquisition officer and the engineer who trained the data—must be unambiguously established to uphold the integrity of international law.¹⁴

11. Conclusion

The pursuit of algorithmic warfare and the deployment of autonomous swarms offer undeniable tactical advantages, providing the U.S. military with essential mass, speed, and operational resilience in heavily contested, A2/AD environments. However, the true barrier to operationalizing these capabilities is not the industrial base’s ability to produce hardware; it is the immense systemic challenge of integrating the Law of Armed Conflict and the Rules of Engagement into software code.

To legally and safely enable warfighters to employ these advanced systems, DoD leadership must definitively shift from a platform-centric acquisition mindset to a software-assurance mindset. This transformation requires embedding legal counsel into the foundational stages of algorithmic design, abandoning outdated static testing methodologies in favor of a continuous evaluation continuum, and enforcing strict, logic-based constraints on probabilistic machine learning models. Without these systemic policy adaptations, the aggressive timeline of fielding autonomous systems risks not only widespread legal violations and ethical failures but the severe, uncontrollable destabilization of global crisis management. Accountability, legal adherence, and ethical principles must be engineered into the code just as deliberately as the physical payload is integrated into the airframe.

Please share the link on Facebook, Forums, with colleagues, etc. Your support is much appreciated and if you have any feedback, please email us in**@*********ps.com. If you’d like to request a report or order a reprint, please click here for the corresponding page to open in new tab.

Sources Used

1. Deputy Secretary of Defense Kathleen Hicks’ Remarks: “Unpacking the Replicator Initiative”, accessed April 24, 2026, https://www.war.gov/News/Speeches/Speech/Article/3517213/deputy-secretary-of-defense-kathleen-hicks-remarks-unpacking-the-replicator-ini/
2. The Autonomous Arsenal in Defense of Taiwan: Technology, Law, and Policy of the Replicator Initiative | The Belfer Center for Science and International Affairs, accessed April 24, 2026, https://www.belfercenter.org/replicator-autonomous-weapons-taiwan
3. Code, Command, and Conflict: Charting the Future of Military AI – Belfer Center, accessed April 24, 2026, https://www.belfercenter.org/research-analysis/code-command-and-conflict-charting-future-military-ai
4. The Coming Military AI Revolution – Army University Press, accessed April 24, 2026, https://www.armyupress.army.mil/Journals/Military-Review/English-Edition-Archives/May-June-2024/MJ-24-Glonek/
5. Rules of Engagement as a Regulatory Framework for Military Artificial Intelligence, accessed April 24, 2026, https://lieber.westpoint.edu/rules-engagement-regulatory-framework-military-artificial-intelligence/
6. Full article: Encoding the Enemy: The Politics Within and Around Ethical Algorithmic War, accessed April 24, 2026, https://www.tandfonline.com/doi/full/10.1080/13600826.2023.2234395
7. Ethical, Legal and Operational Challenges of AI-Driven Warfare and Autonomous Systems > Air University (AU) > Article Display, accessed April 24, 2026, https://www.airuniversity.af.edu/Office-of-Sponsored-Programs/Research/Article-Display/Article/4459074/ethical-legal-and-operational-challenges-of-ai-driven-warfare-and-autonomous-sy/
8. Human-Guided Learning for Probabilistic Logic Models – PMC – NIH, accessed April 24, 2026, https://pmc.ncbi.nlm.nih.gov/articles/PMC7805928/
9. LAWS AND LAWYERS: LETHAL AUTONOMUS WEAPONS BRING …, accessed April 24, 2026, https://tjaglcs.army.mil/Portals/0/Publications/Military%20Law%20Review/2020%20(Vol%20228)/Vol.%20228%20-%20Issue%201/2020-Issue-1-Laws%20and%20Lawyers.pdf?ver=KtuYW-rETtMhd_XYtzwmpA%3D%3D
10. Developmental Test and Evaluation of Autonomous … – USD(R&E), accessed April 24, 2026, https://www.cto.mil/wp-content/uploads/2025/10/DTE-of-AS-GB.pdf
11. The Risks – Autonomous Weapons Systems, accessed April 24, 2026, https://autonomousweapons.org/the-risks/
12. DoD Directive 3000.09, “Autonomy in Weapon Systems,” January 25, 2023 – Executive Services Directorate, accessed April 24, 2026, https://www.esd.whs.mil/portals/54/documents/dd/issuances/dodd/300009p.pdf
13. Pentagon updates guidance for development, fielding and employment of autonomous weapon systems | DefenseScoop, accessed April 24, 2026, https://defensescoop.com/2023/01/25/pentagon-updates-guidance-for-development-fielding-and-employment-of-autonomous-weapon-systems/
14. Legal Accountability for AI-Driven Autonomous Weapons – Lieber Institute – West Point, accessed April 24, 2026, https://lieber.westpoint.edu/legal-accountability-ai-driven-autonomous-weapons/
15. Deep Dive: Pentagon’s Replicator Initiative Raises Questions | Inkstick, accessed April 24, 2026, https://inkstickmedia.com/deep-dive-pentagons-replicator-initiative-raises-questions/
16. How NATO can integrate AI to prevail in future algorithmic warfare – Atlantic Council, accessed April 24, 2026, https://www.atlanticcouncil.org/in-depth-research-reports/report/how-nato-can-integrate-ai-to-prevail-in-future-algorithmic-warfare/
17. January/February 2022 – Collaborative Autonomy, Swarming Advancing in Next Generation Military Drone Systems – Aviation Today, accessed April 24, 2026, https://interactive.aviationtoday.com/avionicsmagazine/january-february-2022/collaborative-autonomy-swarming-advancing-in-next-generation-military-drone-systems/
18. Autonomous Weapons Systems and Proportionality: The Need for Regulation, accessed April 24, 2026, https://scholarlycommons.law.case.edu/cgi/viewcontent.cgi?article=2713&context=jil
19. Train AI Models for Combat: Real-Time Object Classification – Xcelligen Inc., accessed April 24, 2026, https://www.xcelligen.com/how-ai-models-trained-for-object-classification-in-combat-scenarios/
20. DOD Updates Autonomy in Weapons System Directive – Department of War, accessed April 24, 2026, https://www.war.gov/News/News-Stories/Article/Article/3278065/dod-updates-autonomy-in-weapons-system-directive/
21. New US Policy on Autonomous Weapons Flawed | International Human Rights Clinic, accessed April 24, 2026, https://humanrightsclinic.law.harvard.edu/new-us-policy-on-autonomous-weapons-flawed/
22. NOTEWORTHY: DoD Autonomous Weapons Policy – CNAS, accessed April 24, 2026, https://www.cnas.org/press/press-note/noteworthy-dod-autonomous-weapons-policy
23. U.S. Department of Defense Responsible Artificial Intelligence Strategy and Implementation Pathway, accessed April 24, 2026, https://media.defense.gov/2024/Oct/26/2003571790/-1/-1/0/2024-06-RAI-STRATEGY-IMPLEMENTATION-PATHWAY.PDF
24. A Comparative Analysis of the Definitions of Autonomous Weapons Systems – PMC, accessed April 24, 2026, https://pmc.ncbi.nlm.nih.gov/articles/PMC9399191/
25. XIV-MPH.pdf – Międzynarodowe Prawo Humanitarne Konfliktów Zbrojnych, accessed April 24, 2026, https://mph.amw.gdynia.pl/wp-content/uploads/2025/03/XIV-MPH.pdf
26. Can Complementary Learning Methods Teach AI the Laws of War? – CIP, accessed April 24, 2026, https://internationalpolicy.org/publications/can-complementary-learning-methods-teach-ai-the-laws-of-war/
27. Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges – arXiv, accessed April 24, 2026, https://arxiv.org/html/2507.19672v1
28. Rules of Engagement as a Security Protocol and the Challenges posed by Autonomous Weapon Systems – European Consortium for Political Research (ECPR), accessed April 24, 2026, https://ecpr.eu/Events/Event/PaperDetails/77350
29. Synthetic Data for Target Acquisition | ITEA Journal, accessed April 24, 2026, https://itea.org/journals/volume-45-4/synthetic-data-for-target-acquisition/
30. GEOINT Artificial Intelligence, accessed April 24, 2026, https://www.nga.mil/news/GEOINT_Artificial_Intelligence_.html
31. Integrating Artificial Intelligence and Machine Learning Technologies into Common Operating Picture and Course of Action Develop, accessed April 24, 2026, https://press.armywarcollege.edu/cgi/viewcontent.cgi?article=1976&context=monographs
32. Military AI Will Mean Overhauling Test: DOD’s First AI Chief – Air & Space Forces Magazine, accessed April 24, 2026, https://www.airandspaceforces.com/military-ai-overhauling-test/
33. Developmental Test & Evaluation of Artificial Intelligence Enabled Systems, DTE&A – DoW Research & Engineering, OUSW(R&E), accessed April 24, 2026, https://www.cto.mil/dtea/te_aies/
34. Human Systems Integration Test and Evaluation of Artificial Intelligence-Enabled Capabilities, accessed April 24, 2026, https://www.ai.mil/Portals/137/Documents/Resources%20Page/Human%20Systems%20Integration%20Test%20and%20Evaluation%20of%20AI-Enabled%20Capabilities%20Framework.pdf
35. Military Command and Control evolution in an age of human-machine teaming, accessed April 24, 2026, https://nousgroup.com/insights/military-command-and-control-evolution-in-an-age-of-human-machine-teaming
36. Reimagining Military C2 in the Age of AI – Revolution, Regression, or Evolution – Special Competitive Studies Project (SCSP), accessed April 24, 2026, https://www.scsp.ai/wp-content/uploads/2024/12/DPS-Reimagining-Military-C2-in-the-Age-of-AI.pdf
37. Leveraging Human–Machine Teaming – RUSI, accessed April 24, 2026, https://static.rusi.org/human-machine-teaming-sr-jan-2024.pdf
38. Battlefield Applications for Human-Machine Teaming: – Atlantic Council, accessed April 24, 2026, https://www.atlanticcouncil.org/wp-content/uploads/2023/08/Battlefield-Applications-for-HMT.pdf
39. Protecting Warfighters with Drone Swarms: Charles River Analytics’ Mike Farry Presents at Human-Machine Teaming Workshop, accessed April 24, 2026, https://cra.com/protecting-warfighters-with-drone-swarms-charles-river-analytics-mike-farry-presents-at-human-machine-teaming-workshop/
40. HUMAN-MACHINE TEAMING FOR FUTURE GROUND FORCES – CSBA, accessed April 24, 2026, https://csbaonline.org/uploads/documents/Human_Machine_Teaming_FinalFormat.pdf
41. Designing for Doctrine: Decentralized Execution in Unmanned Swarms – Air University, accessed April 24, 2026, https://www.airuniversity.af.edu/Wild-Blue-Yonder/Articles/Article-Display/Article/2703656/designing-for-doctrine-decentralized-execution-in-unmanned-swarms/
42. Swarm Technology in Sustainment Operations | Article | The United States Army, accessed April 24, 2026, https://www.army.mil/article/282467/swarm_technology_in_sustainment_operations
43. Artificial Intelligence and the Future of Strategic Stability – Texas National Security Review, accessed April 24, 2026, https://tnsr.org/roundtable/artificial-intelligence-and-the-future-of-strategic-stability/
44. Artificial Intelligence – Lieber Institute – West Point, accessed April 24, 2026, https://lieber.westpoint.edu/articles-of-war/topics/artificial-intelligence/
45. Impact of Military Artificial Intelligence on Nuclear Escalation Risk – SIPRI, accessed April 24, 2026, https://www.sipri.org/sites/default/files/2025-06/2025_6_ai_and_nuclear_risk.pdf
46. Countering Swarms: Strategic Considerations and Opportunities in Drone Warfare, accessed April 24, 2026, https://ndupress.ndu.edu/Media/News/News-Article-View/Article/3197193/countering-swarms-strategic-considerations-and-opportunities-in-drone-warfare/
47. The Risks of Autonomous Weapons Systems for Crisis Stability and Conflict Escalation in Future U.S.-Russia Confrontations | RAND, accessed April 24, 2026, https://www.rand.org/pubs/commentary/2020/06/the-risks-of-autonomous-weapons-systems-for-crisis.html
48. Resolved: States ought to ban lethal autonomous weapons. – Debate, accessed April 24, 2026, https://debate.utah.edu/high_school_outreach/briefs/LD-20-21-Autonomous-Weapons.docx
49. Autonomous Weapons Systems and the Laws of War | Arms Control Association, accessed April 24, 2026, https://www.armscontrol.org/act/2019-03/features/autonomous-weapons-systems-and-laws-war
50. Review of the 2023 US Policy on Autonomy in Weapons Systems | Human Rights Watch, accessed April 24, 2026, https://www.hrw.org/news/2023/02/14/review-2023-us-policy-autonomy-weapons-systems