The Role of AI in Asymmetric Threats and Military Modernization
The Role of AI in Asymmetric Threats and Military Modernization
I. AI's Impact
The modern battlefield is increasingly shaped by an unseen hand, one that processes information and guides actions at speeds and scales previously unimaginable. Consider the conflict in Ukraine, where artificial intelligence (AI) assists in sifting through vast quantities of drone footage to identify targets, or the rapid deployment of AI-enhanced autonomous systems by various global actors. These are not isolated incidents but rather emblematic of a profound shift in the nature of warfare. The integration of AI is more than just an upgrade to existing military hardware; it represents a fundamental disruptive force, altering the very calculus of conflict.
This technological sea change is reshaping the strategic landscape in a complex, often paradoxical manner. On one hand, AI is supercharging state-led military modernization efforts, promising unprecedented capabilities in command, intelligence, and autonomous operations. On the other, it is simultaneously empowering weaker actors, including non-state groups, with potent asymmetric capabilities that can challenge conventional military might. This dual impact creates an exceptionally intricate and potentially volatile global security environment. The very speed at which AI evolves and is adopted introduces an "acceleration dilemma." Military operations are quickened, but so too is the pace of strategic adaptation required by all actors, compressing decision-making cycles and heightening the risk of miscalculation, particularly if systems are deployed without exhaustive testing or if human oversight is marginalized in the rush to maintain a technological edge.
This report will navigate the labyrinthine implications of AI in modern warfare. It begins by defining the core concepts of asymmetric threats and military modernization as they are being reshaped by artificial intelligence. It will then explore the diverse applications of AI across military domains, examine the geopolitical dynamics of this technological race, dissect the critical risks and ethical quandaries involved, and finally, consider the challenges of governance and the future trajectory of AI on the algorithmic battlefield.
II. Modernization and Asymmetry with AI
Understanding the impact of artificial intelligence on contemporary conflict requires a clear definition of two central concepts: asymmetric threats and military modernization, both of which are being fundamentally altered by this technological wave.
Asymmetric Threats Evolved
Asymmetric threats, often characterized by their non-military nature, have seen a significant rise in the post-Cold War era. They frequently emanate from non-state actors such as regional entities, communities, or individuals, and pose a security risk to all parties involved, often manifesting as a pervasive sense of danger before escalating into direct confrontation. Historically, asymmetrical warfare has encompassed tactics like guerrilla warfare, where lightly armed partisans confront conventional armies, and terrorist actions such as hijackings and suicide bombings. These methods typically involve a smaller, weaker group attacking a stronger one, often by targeting civilians, which by definition constitutes one-way warfare. The core of asymmetry lies not merely in the inequality of forces but in the employment of different kinds of attacks and strategies that conventional military structures find difficult to counter effectively. Victory in such conflicts does not always go to the militarily superior force; indeed, colonial powers often contended with asymmetrical threats where the insurgents' aim was not outright military defeat of the colonizer but rather the erosion of their will to sustain the conflict.
Artificial intelligence is proving to be a significant magnifier of these existing asymmetric threats. Historically, asymmetric warfare relied on exploiting an adversary's weaknesses through unconventional tactics. The advent of AI, particularly low-cost, open-source, or commercially available tools, provides less-resourced actors with access to capabilities that were once the exclusive domain of powerful states. These include sophisticated intelligence gathering, surveillance, and reconnaissance (ISR), precision targeting through armed drones, and the ability to wage large-scale disinformation campaigns. This "democratization" of advanced technology effectively lowers the resource barrier for conducting impactful operations, allowing weaker actors to pose more significant and complex threats. Thus, AI acts as a potent force multiplier for those employing asymmetric tactics, making their unconventional approaches even more challenging for traditional state militaries to anticipate and counter.
AI in Military Modernization
Military modernization is a comprehensive process that involves the transformation and updating of a nation's armed forces through the adoption of new technologies, strategies, and organizational structures. The ultimate goal is to enhance operational effectiveness and readiness. Crucially, this process extends far beyond the simple acquisition of new equipment or materiel. True military modernization necessitates the deep integration of these new assets with doctrine, organizational structures, training methodologies, leadership development, personnel policies, and supporting infrastructure. This holistic approach distinguishes genuine modernization, which prepares an institution for future strategic environments, from mere re-engineering, which might only improve current processes without necessarily positioning the organization to face future challenges.
The integration of AI is driving an unprecedented transformation in military modernization. While AI-driven modernization promises significantly enhanced military capabilities, it concurrently introduces new layers of complexity and vulnerability. The advanced AI systems being developed for command and control, intelligence, and autonomous operations are incredibly intricate, relying on vast datasets, sophisticated algorithms, and highly interconnected networks. This inherent complexity gives rise to a host of new challenges. These include ensuring the integrity and security of massive datasets, preventing and mitigating algorithmic bias, safeguarding AI models against adversarial attacks (such as data poisoning), and navigating the profound ethical implications of autonomous decision-making in lethal contexts. Furthermore, the very interconnectedness required for AI-driven multi-domain operations creates an expanded attack surface with more potential points of failure or exploitation. Consequently, the pursuit of superior military capabilities through AI introduces a modernization paradox: the quest for greater strength and efficiency inherently brings with it new forms of fragility and complex risks that demand diligent management.
III. AI Revolutionizing Military Power
Artificial intelligence is rapidly becoming indispensable across a wide spectrum of military functions, driving a revolution in how armed forces operate, make decisions, and prepare for future conflicts. Its integration is central to the digital modernization efforts of leading military powers, aiming to provide a decisive edge in an increasingly complex global security landscape.
AI in Command & Decision Support
AI is at the heart of efforts to modernize military command and control systems. Its primary role is to enhance commanders' decision-making capabilities and responsiveness by rapidly processing and analyzing vast volumes of data from diverse sources. This capability is crucial in all phases of military operations, including planning, analysis, targeting, execution, and assessment. By creating shared awareness across different operational domains—land, air, sea, space, and cyber—AI-enabled C2 systems aim to provide a clearer, more comprehensive understanding of the battlefield. Lieutenant General (ret.) John Shanahan notes that AI-enhanced C2 should empower humans to make better, more informed decisions, providing a decision advantage even under the intense conditions of peer adversary combat.
The OODA loop (Observe, Orient, Decide, Act), a foundational concept in military strategy, is significantly impacted by AI, particularly in the observation and orientation phases. AI can rapidly collate and interpret sensor data, helping commanders build a richer contextual understanding of a situation before decisions are made or actions are taken. In the near term, Generative AI (GenAI) shows considerable promise for joint and service planning processes. Given advancements in natural language processing, GenAI is likely to achieve better and faster results in planning than in real-time operational execution, potentially accelerating the development and dissemination of strategic guidance and operational orders.
AI in ISR
The field of intelligence, surveillance, and reconnaissance is undergoing a profound transformation due to AI. AI algorithms possess the capacity to analyze massive datasets gleaned from an array of sources—including satellites, drones, social media, and other sensors—to identify patterns, detect threats, and assess battlefield conditions in real-time. This analytical power far surpasses human capabilities in terms of speed and scale. AI-powered systems can sift through vast quantities of data to uncover hidden trends and anomalies that would be impossible for human analysts to detect, thereby improving situational awareness and enabling more informed decision-making. Furthermore, AI is being used to automate many repetitive and data-intensive ISR tasks, which frees up human intelligence analysts to focus on more strategic and complex activities where human judgment and intuition are paramount.
Autonomous Systems
One of the most visible impacts of AI in military modernization is the proliferation of autonomous systems. The US Navy, for instance, is increasingly looking to autonomous systems to address challenges related to the quantity and cost of traditional naval platforms, with a particular interest in collaborative swarms of Autonomous Surface Vessels (ASVs) that can operate with minimal human interaction to deter or counter threats.
AI is dramatically enhancing the capabilities of unmanned systems across all domains. This includes enabling autonomous navigation for drones in GPS-denied or contested environments, improving the precision and speed of targeting systems, facilitating adaptive mission planning where drones can adjust parameters in response to real-time battlefield changes, and coordinating the actions of multiple drones operating as a cohesive "swarm". Examples of these AI-powered systems include Unmanned Aerial Vehicles (UAVs) performing surveillance and strike missions, Unmanned Ground Vehicles (UGVs) undertaking tasks like mine detection or resupply in hazardous areas, and "smart" munitions capable of adapting their trajectories mid-flight based on new intelligence to maximize impact and minimize collateral damage.
The advent of autonomous swarms and low-cost AI-enabled systems presents a significant challenge to traditional military economics. These systems offer the potential for high-volume, attritable forces that can overwhelm or neutralize expensive, high-value legacy platforms, such as large warships or advanced fighter jets. Recent conflicts, like the naval engagements in the Black Sea or the widespread use of drones in Ukraine, have demonstrated how numerous inexpensive systems can inflict disproportionate losses on more costly conventional assets. This "attrition-cost inversion" forces a re-evaluation of platform investment strategies, pushing militaries to consider a shift towards more numerous, expendable, AI-driven systems to maintain operational mass and effectiveness.
AI in Logistics & Maintenance
Beyond combat roles, AI is revolutionizing military logistics and sustainment. The US Defense Logistics Agency (DLA), for example, employs over 55 AI models for tasks such as demand planning, supply chain risk management (including the automated identification of vendors potentially supplying counterfeit or overpriced items), and inventory control to prevent stockouts or overstocking. This use of predictive analytics helps the DLA deliver more timely results and better outcomes, allowing personnel to plan smarter, faster logistics support by reducing guesswork.
AI is also critical for predictive maintenance across a range of military assets, including aircraft fleets, ground vehicles, naval vessels, and missile systems. By analyzing historical and real-time data from sensors, AI algorithms can foresee potential equipment malfunctions before they occur, enabling proactive repairs during planned downtime, thereby reducing operational disruptions, enhancing safety, and optimizing resource allocation. AI-driven logistics platforms are being developed to integrate real-time data from satellites, drones, and ground sensors to track inventory and movement, anticipate supply needs based on mission plans, and dynamically optimize routes to avoid threats or adverse conditions.
AI in Electronic Warfare
The electromagnetic spectrum is a critical domain in modern warfare, and AI is significantly enhancing capabilities in Electronic Warfare (EW). AI enables EW systems to autonomously identify, classify, and respond to hostile signals and electronic attacks with far greater speed and precision than traditional methods. "Cognitive EW systems" are emerging that can learn from the operational environment, adapt their responses in real-time, and even predict adversary actions, allowing them to function effectively in complex and rapidly evolving electromagnetic landscapes. New applications include AI-guided electronic deception, where AI helps create false electronic signatures to mislead adversaries, and adaptive jamming, where AI dynamically adjusts jamming signals to counter specific enemy radar or communication systems.
AI in Training & Simulation
Military training is another area being transformed by AI. AI-driven simulations create highly realistic, immersive, and adaptable training environments that can replicate complex real-world combat scenarios without the risks and costs associated with live exercises. These systems leverage machine learning, natural language processing, and computer vision to deliver intelligent and data-driven training modules. Key trends include AI-powered adaptive learning systems that tailor training difficulty to individual progress, the use of Virtual and Augmented Reality (VR/AR) for immersive simulations, the integration of "digital twins" of battlefield environments for strategic analysis, and specialized AI-driven simulators for cyber warfare training. Canada's Department of National Defence, for instance, views AI for training and simulation as a key capability, with mentions of virtual wargaming and digital twins in its AI strategy.
AI in Space
The space domain is increasingly vital for military operations, and AI plays a crucial role in Space Domain Awareness (SDA). AI and machine learning algorithms are applied to estimate the current and future states of resident space objects, track and catalog tens of thousands of objects in orbit, predict their movements, and improve conjunction assessment (predicting potential collisions). AI enhances space-based ISR capabilities and can autonomously orchestrate the reconfiguration of satellite constellations to ensure the availability of vital communication, navigation, and missile warning systems. However, the technologies underpinning AI for space applications are often dual-use, meaning that capabilities critical for military space operations can also become accessible to other state and non-state actors, adding another layer of complexity to space security.
The following table summarizes key AI applications in military modernization:
IV. AI's Asymmetric Advantage
While nation-states invest heavily in AI for military modernization, the proliferation of AI technologies also significantly empowers a diverse range of non-state actors (NSAs), providing them with an "asymmetric edge." The inherent characteristics of many AI tools—low cost, open-source availability, and dual-use applicability—are lowering the barrier to entry for advanced capabilities that were once the exclusive preserve of well-resourced militaries. This "democratization" of sophisticated technology is enabling criminal organizations, paramilitary groups, terrorist entities, and state-backed proxies to enhance their operational effectiveness in ways that challenge traditional security paradigms.
The development and deployment of increasingly sophisticated AI by state militaries inadvertently contribute to the capabilities available to NSAs. As states drive innovation in AI, the underlying technologies, algorithms, and even trained personnel can become accessible to these groups through various direct and indirect pathways. This includes the commercialization of dual-use AI research, the open-source nature of many AI models, and the potential for state-sponsorship or illicit technology transfer. This dynamic creates a feedback loop where state-driven advancements ultimately accelerate the adoption and adaptation of AI by NSAs, continuously enhancing their disruptive potential.
AI in Disinformation
One of the most prominent asymmetric applications of AI is in the realm of disinformation and cognitive warfare. Generative AI (GenAI) tools are being widely used by both state-affiliated entities and NSAs to create and disseminate propaganda, deepfakes, and other forms of manipulated content at an unprecedented scale and sophistication. For instance, the Russia-affiliated news page DCWeekly.org reportedly used GenAI to significantly increase its output of disinformation, demonstrating how AI can amplify propaganda efforts. Similarly, China's strategic approach to "cognitive warfare" (认知战) leverages AI to shape perceptions and manipulate public opinion long before any kinetic conflict might begin, employing AI-driven predictive modeling, behavioral analytics, and the automated creation of personalized propaganda and deepfakes.
Recent electoral campaigns and political events globally have seen a surge in AI-generated fake images and audio, such as fabricated endorsements or compromising depictions of public figures, aimed at confusing voters and eroding trust. Violent non-state actors (VNSAs) are also exploiting GenAI to enhance the quality and quantity of their propaganda. Examples include ISIS-affiliated publications using more refined GenAI-generated images and the creation of genre-specific extremist music designed to radicalize listeners. AI also facilitates the evasion of content moderation on social media platforms; for example, by overlaying graphic content, like footage from the Christchurch attack, with innocuous imagery to bypass automated detection systems. This ability to generate highly persuasive, tailored content and distribute it rapidly through social media algorithms allows malicious actors to exploit societal divisions, undermine trust in institutions, and incite violence with greater ease and impact.
AI in Cyber Offense
The convergence of AI and cyber operations has equipped attackers with significantly enhanced offensive capabilities. AI algorithms empower malicious actors to launch more sophisticated, automated, and adaptive cyberattacks against a wide range of targets. AI is being employed to create "polymorphic malware" that can continuously alter its code to evade detection by traditional antivirus software. It also automates "zero-day hunting," where algorithms systematically probe software for previously unknown vulnerabilities, allowing even less-skilled attackers to weaponize these exploits more quickly than patches can be developed.
Furthermore, AI is used to craft highly convincing and personalized phishing campaigns. Generative AI can produce emails and messages that flawlessly mimic legitimate corporate communication styles or exploit personal details, significantly increasing their success rate. The emergence of AI-powered cybercrime tools like "WormGPT" and "FraudGPT," marketed on dark web forums, further lowers the barrier to entry for conducting sophisticated cyberattacks. These tools can assist in generating malicious code, crafting deceptive content, and automating various stages of an attack. Non-state actors are increasingly leveraging these AI-assisted techniques to enhance their cyber operations, from data theft and espionage to disruptive attacks on critical infrastructure.
A critical consequence of these AI-driven tools for cyber operations and disinformation is the dramatic lowering of the technical skill required to conduct impactful attacks. Historically, sophisticated cyber operations or large-scale influence campaigns demanded considerable expertise and resources. However, AI tools, particularly generative AI and automated attack platforms, automate many of the complex tasks involved. This automation means that individuals or groups with less technical proficiency can now generate convincing phishing emails, create variants of malware, or launch coordinated disinformation campaigns. This "skill floor lowering" effect broadens the pool of potential malicious actors beyond highly trained specialists, making it easier for a wider range of entities, including those with fewer resources or less training, to pose serious asymmetric threats.
AI and WMD Proliferation
The dual-use nature of artificial intelligence extends alarmingly into the domains of chemical and biological sciences, presenting new pathways for the proliferation of Weapons of Mass Destruction (WMD) capabilities. AI tools designed for beneficial research, such as drug discovery or disease modeling, can be repurposed by malicious actors to design novel pathogens, plan biological attacks, or overcome knowledge gaps in weaponizing chemical or biological agents.
Research has demonstrated the potential for AI, including Large Language Models (LLMs) like ChatGPT, to assist threat actors in these endeavors. For example, a RAND experiment indicated that motivated individuals with the right skills and resources could use LLMs to plan biological attacks, potentially by filling crucial knowledge gaps regarding the harvesting and weaponization of bacteria. More alarmingly, other experiments have shown AI algorithms designing approximately 40,000 new toxic chemical agents in a laboratory setting, including a nerve agent variant reportedly more lethal than VX, simply by inverting the AI's objective from "therapeutic" to "toxic". There are also growing concerns about AI-assisted identification of virulence factors and the in silico (computer-simulated) design of novel pathogens, which could accelerate the development of more dangerous biological weapons. This accessibility of AI-driven design and planning tools significantly lowers the barrier for both state and non-state actors to explore and potentially develop sophisticated chemical and biological threats.
The following table summarizes key AI-powered asymmetric threats:
V. AI in Recent Conflicts
The theoretical potential of artificial intelligence in warfare is rapidly translating into tangible realities on contemporary battlefields. Several recent and ongoing conflicts serve as crucial laboratories, offering stark lessons on AI's capabilities, limitations, and its transformative impact on military operations and strategic calculations.
Ukraine: AI Warfare Lessons
The war in Ukraine has become a crucible for AI-driven warfare, showcasing its extensive application by both sides. The conflict has seen widespread use of various types of drones—including First-Person View (FPV) drones, reconnaissance UAVs, and kamikaze drone swarms—often guided or enhanced by AI for targeting and navigation. This has starkly highlighted the cost-asymmetry that AI-enabled systems can introduce, with relatively inexpensive drones proving effective against far costlier conventional military hardware like tanks and armored vehicles. The sheer volume of data generated, particularly drone footage, is being collected by Ukraine to train AI models for improved battlefield decision-making.
In response to the battlefield dynamics and Ukraine's effective use of Western-supported AI technologies, Russia has reportedly accelerated its own AI integration efforts. This includes enhancing command and control systems, deploying AI-assisted drones like the Lancet in swarming configurations, and bolstering its air defense networks with AI capabilities. The conflict has also underscored the intense interplay of AI in electronic warfare, cyberattacks, and information operations. While AI enhances these capabilities, autonomous systems have proven vulnerable to sophisticated EW tactics, including GPS jamming and signal tracing, which can neutralize drones or lead to the targeting of their operators.
The Ukraine conflict, while demonstrating AI's considerable potential, has also served as a real-world stress test, exposing its current limitations and vulnerabilities in complex, dynamic, and highly contested environments. AI systems in this theater are heavily reliant on data links, GPS signals, and a relatively clear electromagnetic spectrum—all of which are actively disputed and disrupted. The extensive use of EW by both sides significantly impacts drone effectiveness and the reliability of AI-dependent systems. Furthermore, the chaotic nature of combat presents challenges for AI algorithms trained on limited or potentially compromised datasets. This underscores that current AI iterations are not infallible and remain subject to significant operational and environmental constraints, highlighting the continued criticality of human adaptation, robust oversight, and adaptable tactics.
Black Sea Naval Asymmetry
The Black Sea theater has provided compelling evidence of how AI and drone warfare, particularly the use of unmanned surface vessels (USVs) or naval drones, can disrupt traditional naval power dynamics. Ukrainian forces have effectively employed these systems against the Russian Black Sea Fleet, reportedly compelling Russia to withdraw significant naval assets from ports like Sevastopol. This situation illustrates how a technologically adept but less conventionally powerful actor can leverage AI-enhanced autonomous systems to interdict and challenge a larger, more established fleet, fundamentally altering the calculus of maritime warfare in littoral environments.
Azerbaijan-Armenia Conflict
The 2020 conflict between Azerbaijan and Armenia over Nagorno-Karabakh offered an early glimpse into the effectiveness of integrated AI systems on the battlefield. Azerbaijan successfully utilized unmanned aerial systems (UAS), reportedly enhanced with AI-enabled instruments, for precise target pinpointing and subsequent strikes with loitering munitions. This conflict demonstrated the potency of combining advanced ISR capabilities with AI-assisted targeting to achieve decisive military effects, particularly against an adversary with less sophisticated air defenses and countermeasures.
Israel's AI Integration
The Israel Defense Forces (IDF) are undergoing a significant strategic and technological transformation centered on the integration of AI into their operational doctrine. A key element of this is the Matzpen software development unit's AI platform, designed to aggregate and analyze data from a multitude of sensors—including drones, satellites, and cameras—to provide battlefield intelligence and decision support. While Israeli military leaders emphasize that AI will not replace human decision-making, particularly in ethical considerations, the goal is to create an "operational information factory" that can rapidly process information and provide insights to commanders. This includes developing natural language interfaces to allow commanders to interact with AI systems via voice or chat applications, effectively democratizing access to operational intelligence.
Israel is also integrating AI with visualization systems like MapIt, which layers battlefield data onto real-time maps, giving commanders a dynamic, AI-assisted view of threats, targets, and terrain at the tactical level. Furthermore, Israeli defense developers are focusing on AI-powered counter-drone systems and AI-driven detection technologies for complex challenges like counter-tunneling operations, often leveraging experience from high-threat environments like Gaza. Reports from recent operations in Gaza have also indicated the use of AI-assisted targeting systems, such as "Lavender" and "The Gospel," although the specifics and full implications of these systems are still subjects of ongoing analysis and debate. An earlier assessment suggested that Israel's 2021 conflict with Hamas was the "first war to be won via the asymmetric advantage provided by AI". This ongoing adoption reflects a military grappling with the balance between technological innovation, operational effectiveness, and the inherent ethical and control challenges posed by battlefield AI.
VI. Geopolitics of Military AI
The rapid advancements in artificial intelligence and its clear military applications have ignited intense geopolitical competition, with major and emerging powers vying for a technological edge. This dynamic is often characterized as a new AI arms race, driven by the perception that AI could offer a decisive advantage in future conflicts and fundamentally reshape the global balance of power.
United States
The United States Department of Defense (DOD) has explicitly stated its aim to leverage AI to maintain military superiority and ensure national security through comprehensive digital modernization. The strategy focuses on integrating AI-driven technologies to enhance commanders' decision-making, improve combat effectiveness, and protect the defense industrial base from espionage and data breaches. A significant component of this is the US Navy's initiative to develop an AI-enabled hybrid fleet, incorporating large numbers of autonomous systems and fostering partnerships with agile small tech startups to accelerate innovation and manage costs. Recognizing the global implications, the U.S. has also spearheaded initiatives like the Political Declaration on Responsible Military Use of Artificial Intelligence and Autonomy, aiming to build international consensus on responsible behavior in the development and deployment of military AI.
China
China is pursuing an ambitious military AI strategy centered on its "Civil-Military Fusion" (CMF) policy, which aims to seamlessly integrate technological advancements from the private sector directly into the People's Liberation Army (PLA). A core component of this transformation is the development of AI for "cognitive warfare" (认知战), a concept focused on shaping battlefield conditions and manipulating adversary perceptions well before any kinetic engagement. The PLA is leveraging AI for sophisticated predictive modeling, behavioral analytics, the automation of disinformation campaigns, the creation of deepfake technology, and the dissemination of personalized propaganda, particularly targeting regions like Taiwan. Reports suggest China aims to deploy fully autonomous AI weapons systems by 2026 and views AI as essential for rapidly identifying and attacking enemy vulnerabilities. The PLA has also begun deploying AI systems like DeepSeek for non-combat roles, such as in military hospitals and for personnel management, while exploring their applicability to combat scenarios.
Russia
The conflict in Ukraine has served as a catalyst for Russia to accelerate its investment in and integration of AI into its military capabilities. Moscow is significantly increasing its state budget allocation for AI-driven military research, with a focus on enhancing command and control systems, developing AI-powered drones (such as Lancet loitering munitions capable of swarming), and improving air defense networks. There are also indications of efforts to integrate AI into the operations of its Strategic Rocket Forces. This push reflects a broader effort within the Russian military and political establishment to "normalize" the use of AI for military purposes and to close perceived gaps with other major powers, partly by leveraging battlefield experience and international collaborations.
Other Players & Alliances
Beyond the major powers, several other nations and alliances are actively developing and integrating military AI:
India: Is rapidly incorporating AI into its defense sector to modernize military operations, with a focus on AI-powered self-defense technology, including systems like the Indrajal autonomous drone defense system. Key areas include underwater domain awareness, counter-terrorism, and advanced robotics through the Defence Research and Development Organisation's (DRDO) Centre for Artificial Intelligence and Robotics (CAIR). However, India faces challenges such as the need for greater data digitization and ensuring inter-service interoperability for AI systems.
Israel: As detailed previously, Israel is a significant innovator in military AI, particularly in AI-assisted decision support, targeting, counter-drone capabilities, and counter-tunneling technologies, emphasizing human-machine teaming.
South Korea: Is focusing on AI and autonomous systems, partly to address demographic challenges leading to manpower shortages. Its defense innovation plan prioritizes AI, manned-unmanned teaming, and cyber capabilities. South Korea also advocates for a "communication sandbox" to foster defense science and technology collaboration among allies, aiming for standardized frameworks for network information and cybersecurity.
NATO: The North Atlantic Treaty Organization views AI as a profound technological enabler that will improve operational efficiency, facilitate autonomy, and enable more informed military decision-making. NATO's framework categorizes AI development in "waves"—from knowledge-based systems to statistical learning and contextual adaptation. The Alliance is focused on AI applications in pattern recognition (including social media analysis and anomalous behavior detection), automated target recognition (especially for CBRN threats), autonomous systems across air, sea, and land, and predictive maintenance and logistics. A critical priority for NATO is ensuring the interoperability of AI systems among its member states to maintain cohesive collective defense in an era of renewed strategic competition, particularly with Russia.
While individual nations and alliances forge ahead with their military AI programs, a significant emerging challenge is ensuring deep and meaningful interoperability of these complex systems among allies. Effective coalition warfare, a cornerstone of Western military strategy, depends on the seamless exchange of data and coordinated action. However, AI systems are built upon specific data formats, communication protocols, and are often embedded within distinct operational doctrines. If allied nations develop AI systems based on divergent ethical guidelines, disparate data sharing policies, or incompatible technical standards, these systems may struggle to "talk" to each other or collaborate effectively on a fast-paced, AI-suffused battlefield. South Korea's call for a "communication sandbox" and a standard framework for information and cybersecurity implicitly acknowledges this hurdle. Thus, beyond national AI development, achieving true AI interoperability within alliances is a critical geopolitical and operational imperative for maintaining collective defense effectiveness.
Arms Race & Escalation
The pursuit of military AI is characterized by strong arms race dynamics. The belief that AI offers a decisive military advantage fuels competition, as nations strive to avoid falling behind. This race is not only about developing superior algorithms but also about amassing vast datasets for training AI models and building the computational infrastructure to support them.
A primary concern is that AI could dangerously accelerate the pace of warfare and attacks on command-and-control systems, potentially leading to rapid, unintended escalation. The dual-use nature of AI technologies and their relatively lower entry barriers compared to traditional strategic weapons (like nuclear arms) complicate established arms control paradigms, making verification and enforcement difficult. The global AI landscape is seeing both "vertical proliferation" (the continuous refinement of cutting-edge AI systems by technologically advanced nations) and "horizontal proliferation" (the spread of AI capabilities to a wider range of state and non-state actors). This diffusion of AI power, particularly to actors who may not adhere to established norms of responsible behavior, contributes to strategic instability and increases the unpredictability of future conflicts.
The following table offers a comparative overview of national and alliance-level military AI strategies:
VII. Risks and Ethics of Military AI
The integration of artificial intelligence into military affairs, while promising enhanced capabilities, is fraught with profound risks and ethical dilemmas. These challenges stem from the inherent nature of AI technologies, their potential for misuse, and the complexities of maintaining human control and accountability in increasingly automated conflict environments.
Escalation & "Flash Wars"
A significant concern is the potential for AI-driven systems to trigger rapid, uncontrollable escalation in a crisis. Interactions between multiple AI systems, or between AI and human operators under extreme time pressure, could lead to unforeseen and cascading actions that humans cannot predict or effectively intervene in. This has drawn parallels to the 2010 financial "flash crash," where interacting high-frequency trading algorithms caused a sudden and severe market disruption, demonstrating how automated systems can drive rapid, uncontrollable dynamics. A "flash war" could erupt even more quickly on the volatile modern battlefield, where AI systems processing information and making tactical adjustments in fractions of a second might react to each other in unpredictable ways.
The risk is compounded by the fundamental goal often programmed into military AI systems: to maximize the probability of mission success or "winning." In complex strategic interactions, actions that appear rational for an AI in isolation (like a preemptive strike based on its threat assessment) could lead to disastrous long-term outcomes if they provoke an escalatory response from an adversary. Furthermore, the increasing entanglement of conventional and nuclear command, control, and communication (C3) systems with AI introduces new pathways to nuclear escalation. If AI-driven systems misinterpret an adversary's actions or inadvertently target assets that are critical to an opponent's nuclear deterrent, it could lead to an unintended or inadvertent escalation to nuclear weapons use, even if the initial conflict was conventional.
Algorithmic Bias
Algorithmic bias represents a critical flaw in military AI systems, with implications that are both ethical and operational. Bias can be introduced at multiple stages of an AI's lifecycle: in the data sets used for training (which may over- or under-represent certain groups or reflect historical prejudices), in the design and development choices made by engineers, in how the AI is used in specific contexts, and even in post-use review processes.
Examples of such bias include facial recognition systems exhibiting higher misidentification rates for individuals with darker skin or for certain genders, which could lead to wrongful targeting or surveillance. In counter-terrorism applications, AI systems might develop biased assumptions, for instance, incorrectly flagging individuals as extremist based on religious observance or ethnic origin if the training data reflects such prejudices. The problem is often exacerbated in military contexts where high-quality, representative training data for all possible combat scenarios may be scarce or difficult to obtain, leading AI systems to make flawed generalizations or misinterpret novel situations.
This algorithmic bias is not merely an ethical concern; it is a significant operational vulnerability. Biased AI systems can lead to incorrect targeting of adversaries or, catastrophically, of civilians or friendly forces. They can produce flawed intelligence assessments by prioritizing or dismissing information based on biased parameters, and ultimately, they can contribute to mission failure or unintended escalation. Adversaries aware of such biases in an opponent's AI systems could potentially exploit these weaknesses by feeding manipulated data or creating scenarios designed to trigger flawed responses. Therefore, identifying and mitigating algorithmic bias is crucial not only for upholding legal and ethical standards but also for ensuring the reliability, effectiveness, and strategic robustness of military AI.
Data Security & AI Attacks
AI systems, particularly those based on machine learning, are heavily reliant on vast quantities of training data. This dependency creates significant vulnerabilities to data security breaches, specifically through "data poisoning" and "model manipulation". Data poisoning involves deliberately corrupting the datasets used to train AI models by injecting malicious or biased data. This can cause the AI to learn incorrect patterns, make flawed decisions, or generate harmful outcomes.
Several types of data poisoning attacks exist:
Targeted Poisoning: Malicious data is inserted to cause errors for specific scenarios or targets. For example, an AI system designed to detect malware might be poisoned to ignore a particular type of threat.
Backdoor Attacks: Hidden triggers are embedded in the training data. The AI model behaves normally until a specific input activates the backdoor, causing it to bypass security controls or execute malicious functions.
Label Flipping: Attackers manipulate the labels in training data (e.g., labeling a malicious file as benign), causing the AI to learn incorrect classifications.
Data Injection: Fabricated data points are introduced to steer the AI model's behavior in a desired direction.
Clean-Label Attacks: Subtle modifications are made to training data that are imperceptible to humans but can cause the AI model to misbehave on specific inputs.
Model manipulation attacks exploit trained AI systems to infer or reconstruct confidential information from the model itself, or to trigger unintended behaviors. The AI supply chain also presents risks, as third-party pre-trained models or datasets could be compromised before they are even acquired. Such attacks can have severe consequences, from spreading targeted misinformation if content recommendation algorithms are compromised, to misdiagnoses in healthcare, or catastrophic failures in autonomous vehicles or weapons systems.
LAWS: Control & Accountability
Lethal Autonomous Weapons Systems (LAWS) are broadly defined as weapon systems that, once activated, can independently search for, identify, select, and engage targets without further human intervention. The development and potential deployment of LAWS are at the forefront of ethical and legal debates surrounding military AI. The core concern is the prospect of machines making life-and-death decisions on the battlefield, effectively removing meaningful human control from the use of lethal force.
This leads to a critical "accountability gap". Under existing legal frameworks, assigning personal criminal or civil liability for unlawful actions committed by LAWS presents significant hurdles. Military commanders or operators might evade responsibility if they could not reasonably foresee an autonomous weapon's unlawful actions or were unable to intervene to prevent them, particularly if the system malfunctioned or exhibited unexpected emergent behavior. Programmers and manufacturers may also be shielded by legal immunities or the difficulty of proving direct causation or negligence for complex AI failures. The consequences of this accountability vacuum are dire: a lack of deterrence against future unlawful uses of LAWS, no retribution or justice for victims, and an absence of social condemnation for those who might be morally responsible.
AI Proliferation Risks
The dual-use nature of AI technology, combined with its increasing accessibility and decreasing cost, significantly heightens proliferation risks. Advanced AI capabilities, which can be used for beneficial civilian purposes, can also be readily adapted for malicious military applications by a wide range of actors, including states with questionable intentions, non-state armed groups, terrorist organizations, and criminal enterprises.
The phenomenon of "AI convergence"—where AI amplifies the risks associated with other dangerous technologies such as biological, chemical, nuclear, and cyber weapons—is particularly concerning. AI can lower the barriers to developing or acquiring WMDs by assisting in the design of pathogens or chemical agents, automating complex research processes, or disseminating sensitive knowledge. Similarly, AI can be used to develop more potent and evasive cyberweapons or to enhance the capabilities of autonomous delivery systems for various payloads.
Efforts to counter AI proliferation are emerging, including state strategies focused on "compute security" (e.g., geofencing AI chips, implementing licensing and remote attestation for hardware) and "AI security" (e.g., developing techniques to make AI models more controllable, robust, and resistant to tampering). International bodies like the African Union have also expressed concerns about the misuse of AI by non-state actors and are calling for human-rights-centered governance frameworks and national cybersecurity strategies to mitigate these risks. However, the ease with which AI knowledge and tools can spread, often through open-source channels or commercial markets, makes controlling its proliferation an exceptionally difficult challenge.
The following table outlines key risks and ethical challenges associated with military AI:
VIII. Governance and Future of Military AI
The rapid integration of artificial intelligence into military arsenals and its proliferation among diverse actors present an urgent and complex governance challenge. As AI technologies outpace traditional regulatory frameworks, the international community, national governments, and alliances are grappling with how to establish norms, rules, and ethical guidelines to mitigate the profound risks while potentially harnessing some benefits.
International Governance Efforts
At the forefront of international discussions is the United Nations Group of Governmental Experts (GGE) on Lethal Autonomous Weapons Systems (LAWS). Convened under the Convention on Certain Conventional Weapons (CCW), the GGE's mandate includes exploring and agreeing on possible recommendations related to emerging technologies in LAWS. Key discussion points have revolved around ensuring compliance with International Humanitarian Law (IHL), defining the parameters of meaningful human control over the use of force, and establishing a common understanding and definition of LAWS. While consensus on a legally binding treaty remains elusive, the GGE has made some progress, notably around a "two-tiered approach" involving potential prohibitions on certain types of LAWS (e.g., those that function without human control or cannot comply with IHL) and regulations for others. A UN General Assembly resolution adopted in December 2024, with overwhelming support, also mentioned this potential two-tiered framework, reflecting heightened international concern. The UN Secretary-General has called for the conclusion of negotiations on a new international treaty on LAWS by the end of 2026.
Another significant initiative is the Political Declaration on Responsible Military Use of Artificial Intelligence and Autonomy, launched in February 2023 at the Responsible AI in the Military Domain (REAIM) Summit. Spearheaded by the United States and the Netherlands, this non-binding declaration aims to build international consensus around responsible behavior and guide states in the development, deployment, and use of military AI. It provides a normative framework and a basis for exchanging best practices.
The International Committee of the Red Cross (ICRC) has been a prominent voice, consistently calling for new, legally binding international rules to prohibit unpredictable autonomous weapons and those designed to apply force against persons, and to place strict restrictions on all other types of LAWS. The ICRC emphasizes the imperative of preserving human control over life-and-death decisions to uphold IHL and fundamental ethical principles.
Despite these and other forums, such as SIPRI's engagement in discussions on military AI governance, a critical "governance gap" persists. The pace of AI technological development and its military adoption far exceeds the speed at which comprehensive and binding international governance frameworks are being established. This disparity creates a dangerous lacuna where potentially destabilizing AI applications could proliferate without adequate oversight, control, or universally accepted red lines.
National & Alliance Ethics
In parallel with international efforts, individual nations and alliances are developing their own ethical frameworks and policies for military AI. The U.S. Department of Defense has established a "Responsible AI" (RAI) framework and is actively collaborating with allies to promote these principles. Key U.S. allies, including France, Australia, the United Kingdom, and Canada, are also formulating their national approaches to ethical and responsible AI in defense, though with varying degrees of articulation and emphasis.
NATO is actively pursuing a responsible military AI agenda, focusing on ensuring that AI adoption within the Alliance adheres to ethical principles and enhances interoperability. The European Union, through its landmark AI Act, has adopted a human-centric, risk-based model for AI governance. However, the EU AI Act explicitly excludes AI systems developed or used exclusively for military purposes from its scope, though it covers dual-use AI systems that might also have civilian applications. The European Parliament has separately called for stronger regulation of military AI and a prohibition on LAWS.
Regulating Dual-Use AI
A fundamental hurdle in governing military AI is the pervasive dual-use nature of most AI technologies. Algorithms, software, and even hardware developed for civilian applications can often be readily adapted for military purposes, and vice versa. This makes it exceptionally difficult to craft regulations that effectively curb military misuse without stifling beneficial civilian innovation or legitimate defense applications. Verifying compliance with any agreed-upon restrictions is also challenging, particularly regarding covert development programs or the subtle repurposing of commercial AI tools by state or non-state actors.
Future AI Landscape (2030s)
Looking ahead, experts anticipate that AI will become an even more integral and transformative component of military power by the 2030s. AI is expected to be an indispensable force multiplier, revolutionizing predictive decision-making, enabling highly coordinated collaborative autonomous systems across multiple domains, and optimizing dynamic resource management for logistics and sustainment. The concept of "intelligentized warfare," where AI systems may increasingly contend directly against opposing AI systems, is projected to become a reality.
The global military AI market is forecast for continued substantial growth, with one estimate suggesting a rise from $9.86 billion in 2024 to $17.65 billion by 2028. Drones, heavily reliant on AI, are predicted to constitute around 80% of unmanned or remotely-piloted air capabilities by 2030. Emerging AI paradigms, such as Neuro-Symbolic AI (which combines neural networks with symbolic reasoning), are expected to drive new battlefield capabilities, enhancing threat detection, tactical decision-making, and situational awareness.
However, this AI-suffused future is not without its perils. Concerns persist about the potential for overreliance on AI systems, which could erode the critical thinking skills and independent judgment of military leaders. The complex interplay of AI with other global trends, such as robotics and climate change, is also predicted to reshape the landscape of future conflicts, potentially exacerbating resource scarcity and political instability, thereby sparking new types of AI-influenced confrontations.
