By: Khushbu Ahlawat, Consulting Editor, GSDN
Introduction
Artificial intelligence (AI) is rapidly transforming the character of conflict in the twenty-first century, reshaping how violence is conceived, executed, and countered. While states increasingly deploy AI to enhance military precision, intelligence gathering, and strategic forecasting—such as integrating machine learning into drone navigation and battlefield awareness in the Ukraine conflict—non-state actors are also beginning to leverage similar technologies to offset traditional material disadvantages. For instance, extremist networks have experimented with AI-enhanced recruitment and propaganda, using generative models to tailor content and exploit recommendation algorithms to radicalise vulnerable individuals online. Recent research notes that violent non-state actors can exploit AI-driven behavioural profiling and automated messaging to identify and target psychologically susceptible populations in ways that circumvent traditional counter-terrorism methodologies.
These developments mark a decisive shift in asymmetric warfare. AI not only reduces barriers to entry and compresses operational timelines, but it also amplifies both the psychological and physical impact of violence. Global insurgent and extremist groups have published guides on using generative AI for propaganda and disinformation, and analysts have documented cases where manipulated images and videos circulated in major conflicts like Gaza, undermining verified information and escalating mistrust. Meanwhile, military actors are employing AI to automate and accelerate targeting decisions, demonstrating how deeply embedded intelligent systems now are in high-intensity conflict environments.
Scholars warn that this dual-use nature of AI complicates the future of state security. Some argue that AI blurs traditional boundaries between peace and conflict by enabling non-state actors to mimic capabilities once reserved for sophisticated militaries, while others emphasise the need for interdisciplinary frameworks to anticipate ethical, legal, and strategic challenges posed by AI-enabled asymmetric threats. As a result, the traditional gap between state security institutions and adversarial groups is narrowing in unprecedented ways. The emerging “algorithmic battlefield” is therefore not confined to physical combat zones but extends across cyberspace, digital information ecosystems, and autonomous systems. This evolution is reshaping the future of state security, with profound implications for how conflicts will be fought, deterred, and regulated in the years ahead.
Technological Diffusion and Asymmetric Adaptation
Asymmetric warfare has historically been defined by innovation—where weaker actors compensate for limited resources through unconventional tactics. In the contemporary era, artificial intelligence functions as a force multiplier within this logic. Machine learning tools enhance surveillance, reconnaissance, target identification, and operational planning. What once required significant infrastructure and specialised expertise can now be accessed through commercially available software and open-source platforms. The conflict in Ukraine has demonstrated how relatively low-cost AI-enabled drone systems, satellite imagery, and open-source intelligence tools can reshape battlefield dynamics, enabling smaller units to conduct precision strikes and real-time targeting once reserved for technologically advanced militaries. Civilian drone technologies adapted with AI-assisted navigation and image recognition have blurred the line between improvised capability and formal military hardware.
Beyond Ukraine, the use of commercial drones by Houthi movement in the Red Sea has illustrated how non-state actors integrate AI-supported guidance systems and remote sensing technologies to disrupt maritime security. Similarly, during the Gaza conflict, armed groups operating in Gaza Strip utilised commercially modified unmanned systems for surveillance and coordinated attacks, while digital tools amplified operational messaging. These examples underscore how accessible technologies—paired with algorithmic optimisation—enable actors with limited conventional power to generate disproportionate strategic effects.
The diffusion dynamic is not limited to physical battlefields. In Myanmar, resistance groups have reportedly relied on open-source mapping, encrypted communications, and AI-assisted drone modification to counter a conventionally superior military. Meanwhile, transnational extremist networks have experimented with generative AI platforms to produce multilingual propaganda, automate recruitment messaging, and analyse online behavioural data to identify potential supporters. What emerges is a pattern in which AI reduces entry barriers, allowing dispersed actors to access tools that enhance coordination, targeting precision, and psychological impact.
Scholars such as Lawrence Freedman argue that technological diffusion disrupts established hierarchies of power before regulatory frameworks catch up. Audrey Kurth Cronin similarly notes that terrorist innovation often reflects opportunistic adaptation rather than ideological transformation. Contemporary examples reinforce this observation: AI is rarely invented by non-state actors, but it is rapidly adapted by them. AI-driven systems enable faster decision-making, improved coordination, and adaptive strategies in real time, accelerating the cycle of action and reaction between state and non-state actors. Tactical innovations may gradually reshape organisational structures and long-term strategic objectives, enabling smaller groups to operate with greater sophistication and reduced visibility. While states retain superior material capabilities, non-state actors increasingly gain disruptive agility through algorithmic tools, narrowing asymmetries in speed, reach, and informational dominance.
The Information Domain: Algorithmic Propaganda and Cognitive Warfare
The algorithmic battlefield extends deeply into the information domain. Generative AI tools allow extremist networks to produce persuasive propaganda, synthetic media, and multilingual messaging at scale. Platforms such as Telegram and X have been used to circulate AI-generated visuals, automated recruitment narratives, and emotionally tailored messaging. During recent conflicts in Gaza and Ukraine, manipulated videos and AI-generated imagery circulated widely, complicating verification processes and intensifying polarisation.
The strategic implications are profound. Scholars such as P.W. Singer and Emerson T. Brooking argue that modern conflicts are increasingly fought in the “battlefield of the mind,” where perception management rivals physical force. AI-driven misinformation and disinformation campaigns can incite violence, destabilise democratic institutions, and erode public trust without requiring direct confrontation. The “liar’s dividend” phenomenon illustrates how deepfakes allow genuine evidence to be dismissed as fabricated, creating epistemic instability. In this environment, narrative dominance becomes as strategically vital as territorial control, transforming cyberspace into a central arena of cognitive warfare.
Autonomous Systems and the Militarisation of AI
Beyond the digital sphere, AI integration into unmanned systems marks a significant transformation in asymmetric warfare. AI-enabled drones enhance precision targeting, autonomous navigation, and real-time battlefield analytics. The ongoing war in Ukraine demonstrates this shift vividly. Both Ukraine and Russia have deployed AI-assisted drone systems for surveillance, targeting, and coordinated strikes. Ukraine’s use of commercially adapted drones integrated with AI-based image recognition systems illustrates how relatively low-cost technologies can offset conventional military asymmetries. Similarly, Russia’s loitering munitions and autonomous strike drones reflect increasing reliance on algorithm-supported battlefield operations.
The use of autonomous and semi-autonomous drones by non-state actors further underscores this trend. Houthi forces in Yemen have deployed AI-assisted drone and missile systems against Saudi and Emirati infrastructure, including attacks on energy facilities in Saudi Arabia. These incidents reveal how commercially accessible technologies can be repurposed for strategic disruption. Likewise, armed groups across Iraq and Syria have used modified drones for reconnaissance and precision attacks, blurring the line between state and non-state technological capabilities.
In parallel, major powers are institutionalising AI within formal military doctrines. The United States Department of Defense has integrated AI into Project Maven to enhance real-time target identification through machine learning. China has advanced its concept of “intelligentized warfare,” embedding AI into autonomous defence platforms and decision-support systems. Meanwhile, Israel has reportedly employed AI-driven targeting support systems in operations in Gaza Strip, accelerating strike cycles and automating aspects of target selection.
Strategic thinkers warn that autonomous weapons reduce human control in critical decision loops, increasing both operational speed and escalation risks. The deployment of loitering munitions such as the Switchblade system and other semi-autonomous platforms reflects a broader shift toward human-out-of-the-loop or human-on-the-loop warfare models. Swarm technologies—tested by China and the United States—demonstrate how coordinated algorithmic communication between multiple drones can overwhelm conventional defence systems at relatively low cost.
Critical infrastructure—including energy grids, transport networks, and communication systems—has become increasingly vulnerable to AI-enabled disruption. The 2022–2024 attacks on Ukrainian energy infrastructure, combining cyber operations with drone strikes, illustrate the fusion of digital and kinetic AI-enabled warfare. The psychological dimension of these systems further magnifies their strategic effect: autonomous capabilities project technological sophistication and unpredictability, generating fear disproportionate to their material cost.
This evolution signals a broader transformation toward technologically mediated, non-contact forms of violence, where algorithmic systems compress decision-making timelines, decentralise destructive capacity, and blur accountability. As AI becomes embedded within military command architectures, the distinction between human judgment and machine calculation grows increasingly tenuous—raising profound ethical, strategic, and governance challenges for the international system.
Implications for State Security and Counter-Terrorism
The integration of AI into asymmetric warfare presents a multidimensional challenge for state security. Detection and attribution become increasingly complex in an environment saturated with synthetic content and automated systems. The rapid speed of algorithmic operations compresses response timelines for intelligence agencies, demanding anticipatory rather than reactive strategies. Regulatory frameworks struggle to keep pace with the dual-use and globally accessible nature of AI technologies, particularly as private-sector innovation often outpaces public governance.
Technological interdependence creates networked vulnerability, where security threats are embedded within global supply chains and digital infrastructures. At the same time, expanding surveillance to counter AI-enabled threats raises serious concerns regarding civil liberties and democratic accountability. States thus confront a strategic dilemma: how to strengthen algorithmic defence mechanisms without undermining the normative foundations of governance.
Building resilience against cognitive manipulation, investing in AI-driven countermeasures, strengthening digital literacy, and enhancing international regulatory cooperation will be as critical as conventional military preparedness. The algorithmic battlefield represents not merely a technological evolution but a structural transformation in the conduct of asymmetric conflict.
The Governance Vacuum: When Innovation Outpaces Regulation
The rapid diffusion of artificial intelligence has created a governance gap where technological innovation advances faster than regulatory frameworks. Unlike conventional weapons, AI systems are embedded in civilian ecosystems—cloud platforms, commercial drones, open-source software, and global supply chains. This dual-use nature makes control mechanisms complex and fragmented.
Current global debates around autonomous weapons systems reveal deep divisions among major powers. While some states advocate binding international restrictions, others prioritise strategic advantage and deterrence. The absence of enforceable global norms creates structural vulnerabilities that non-state actors can exploit.
Power in the digital age increasingly flows through networks rather than hierarchies. AI technologies operate within transnational innovation ecosystems, making unilateral regulation insufficient. Unchecked autonomy in weapon systems may destabilise global security by lowering the threshold for violence and accelerating escalation. The governance vacuum therefore becomes part of the battlefield itself.
The Legitimacy Dilemma: Security, Surveillance, and Democratic Strain
As states respond to AI-enabled asymmetric threats, they confront a deeper legitimacy challenge. Countering algorithmic warfare often requires enhanced digital surveillance, predictive analytics, biometric monitoring, and AI-driven intelligence systems. While these measures strengthen detection and response capabilities, they also risk infringing upon civil liberties and democratic norms.
This creates a legitimacy dilemma: the very tools designed to protect state security can erode public trust if deployed without transparency and accountability. When AI determines threat assessment or targeting parameters, responsibility becomes legally and ethically blurred.
In an increasingly digitised world where online and offline realities converge, security policy extends beyond territorial defence into cognitive and informational governance. Expanding algorithmic monitoring to counter radicalisation may strengthen short-term resilience but risks normalising permanent surveillance infrastructures. Thus, the algorithmic battlefield is not only about capability competition but about normative endurance.
Conclusion
The rise of the algorithmic battlefield marks a structural inflection point in the evolution of asymmetric conflict. Artificial intelligence is no longer a peripheral tool in warfare; it is becoming an embedded architecture shaping how power is exercised, contested, and legitimised. From AI-enabled drone warfare to synthetic propaganda circulating across digital ecosystems, the diffusion of algorithmic capabilities has narrowed the historical gap between state and non-state actors. Violence is increasingly mediated through code—faster, scalable, and harder to attribute.
Yet the strategic challenge extends beyond operational disruption. AI compresses decision-making cycles, complicates verification, and erodes the informational foundations upon which democratic governance rests. In this environment, superiority is defined not solely by firepower but by adaptive capacity—technological, regulatory, and normative.
The central dilemma for states is therefore twofold: how to outpace adversaries in innovation while preserving legitimacy at home and credibility abroad. Over-securitisation risks normalising intrusive surveillance and weakening democratic resilience; under-regulation risks ceding strategic advantage to agile non-state actors. Ultimately, the future of state security will not be determined only by who builds the most advanced algorithms, but by who governs them most responsibly. The algorithmic battlefield is as much a contest over norms and legitimacy as it is over capability.
About the Author
Khushbu Ahlawat is a research analyst with a strong academic background in International Relations and Political Science. She has undertaken research projects at Jawaharlal Nehru University, contributing to analytical work on international and regional security issues. Alongside her research experience, she has professional exposure to Human Resources, with involvement in talent acquisition and organizational operations. She holds a Master’s degree in International Relations from Christ University, Bangalore, and a Bachelor’s degree in Political Science from the University of Delhi.
