That gap undermines American extended deterrence because Washington cannot reassure allies or deter aggressors without accounting for South Asia’s threat calculus. AI arms developments in this region stem from colonial legacies and mistrust of great power intentions, creating a volatile strategic environment.

India’s Governance Innovation in Defense AI

India’s governance model integrates civilian oversight with defense research and ensures ethical deployment of AI. The Responsible AI Certification Pilot evaluated algorithms for explainability before clearance. Its National Strategy for AI mandates ethical review boards for dual-use systems. Developers must document bias-mitigation measures and escalation pathways. Embedding accountability at design phase stabilizes deterrence signals by reducing inadvertent algorithmic behaviors.

The Evaluating Trustworthy AI (ETAI) Framework advances defense AI governance. It enforces five principles: reliability, security, transparency, fairness, privacy, and sets rigorous criteria for system assessment. Chief of Defense, Staff General Anil Chauhan, stressed resilience against adversarial attacks, highlighting the challenge of balancing effectiveness and safety. By mandating continuous validation against evolving threat scenarios, ETAI prevents mission creep and maintains operational integrity under stress.

India’s dual use by design philosophy embeds safeguards within prototypes from inception. This contrasts with reactive models that regulate AI after deployment. Civilian launch-authorization channels separate political intent from technical execution, ensuring decisions remain under human control and reinforcing credibility in crisis moments. Regular red-team exercises involving independent experts further validate system robustness and reduce risks of false positives in autonomous targeting.

Strengthening Extended Deterrence through Cooperation

US-India collaboration on AI verification can reinforce extended deterrence by aligning technical standards and testing protocols. The iCET fact sheet outlines secure information sharing and joint safety trials. Launched in January 2023, iCET has already enabled co-production of jet engines and transfer of advanced drone technologies. Building on this foundation, specialized working groups could develop common benchmarks for adversarial-resistance testing and automated anomaly detection.

A Center for Strategic and International Studies report recommends a trilateral verification cell blending American evaluation tools with India’s ethical reviews. Joint trials of autonomous air-defense algorithms would demonstrate interoperability and resolve. A shared “AI Red Flag” system would alert capitals to anomalous behaviors and reduce strategic surprise. Embedding cryptographically secure logging of decision path data ensures an immutable audit trail for post-event analysis and confidence building.

The INDUS-X initiative, launched during Prime Minister Narendra Modi’s 2023 US visit, integrates responsible AI principles into defense innovation. By aligning standards, both countries ensure AI systems enhance strategic stability rather than undermine it. Expanding INDUS-X to include scenario-based wargaming with allied partners can stress-test ethical frameworks and calibrate thresholds for human intervention under duress. This model can extend under the Quad framework, pressuring authoritarian regimes to adopt transparency measures.

Institutionalizing Global AI Arms Control

A formal arms control dialogue should adopt India’s baseline standards for ethical AI governance. The UNIDIR report calls for universal bias audits and incident-reporting obligations to prevent unintended escalation. Carnegie scholars propose a tiered certification process under a new protocol for autonomous systems within the Convention on Certain Conventional Weapons, requiring peer review of algorithms before deployment. Embedding such certification in national export-control regimes would create global incentives for adherence.

The UN General Assembly has established an Independent AI Scientific Panel and a Global Dialogue on AI Governance to issue annual assessments on risks and norms. This mechanism can evaluate military AI applications and recommend confidence-building measures. Procedural transparency would coexist with confidentiality requirements, balancing security with mutual reassurance. Regular joint workshops on risk-assessment methodologies can diffuse best practices and diffuse mistrust among major powers.

Regional Applications and Future Prospects

India’s responsible AI framework must inspire regional adoption and confidence-building measures. Pakistan and China should engage transparency initiatives to prevent dangerous asymmetries in AI capabilities. Proposed measures include joint research on AI safety, shared performance databases, and collaborative development of detection algorithms.

Successful tests of India’s hypersonic ET-LDHCM system, capable of Mach 8 and a 1,500-kilometer range, underscore the urgency of governance frameworks before fully autonomous weapons deploy. The Quad’s model of Indo-Pacific cooperation provides a template for multilateral norms on responsible AI in defense. Extending these norms to confidence-building measures such as pre-deployment notifications and automated backchannels can reduce the risk of inadvertent escalation.

Looking ahead to the United Nations General Assembly meeting on AI governance in September 2024, American policymakers can leverage India’s experience. Joint verification exercises and an ethical audit regime will establish global norms for military AI. Integrating lessons from ETAI and iCET into the assembly’s resolutions can produce enforceable standards that bind both democratic and authoritarian states. This approach will reaffirm American extended deterrence and help prevent destabilizing AI-driven arms races worldwide.

By demonstrating that ethical AI development strengthens rather than weakens deterrence credibility, India’s model provides both technical solutions and normative frameworks for managing the military applications of artificial intelligence. Sustained international cooperation on these principles is pivotal for securing strategic stability in a rapidly evolving technological landscape.

Vaibhav Chhimpa is a researcher who previously worked with the Department of Science & Technology (DST), India. Views expressed are the Author’s own.

The Artificial Intelligence (AI) Arms Race in South Asia was originally published on Global Security Review.

Whether the MSS is indicative of an unfolding RMA remains a subject of debate. At a minimum, it represents a significant leap in how modern militaries sense, decide, and act in combat. From a scholarly perspective, RMAs are not defined by single technological breakthroughs but by clusters of innovations that fundamentally transform the conduct of warfare.

They typically involve shifts in doctrine, tactics, organization, culture, and technology. Unlike broader military revolutions, which reshape societies and political systems, RMAs are confined to the military sphere—and they often unfold quietly, only recognized in hindsight.

Several RMAs were identified in the past, providing a framework to anticipate future ones. In The Dynamics of Military Revolutions: 1300–2050, MacGregor Knox and Williamson Murray outline five significant military revolutions in the West since 1618. Each one, they argue, set off a chain of revolutionary changes in military affairs.

These include the emergence of the modern state with its standing armies, the political and social upheavals brought on by the French Revolution, the industrialization of warfare in the 19th century, the era of total war in the 20th century, and the transformative impact of nuclear weapons. If a new RMA is underway, we may not fully recognize it until it has already matured.

The concept of RMA has drawn justified criticism for being abstract, amorphous, and debated to the point of analytical paralysis. After the Gulf War, the DoD’s fixation on identifying the “next RMA” often overshadowed the operational impact of emerging capabilities. Scholars frequently focus on definitional purity rather than assessing real battlefield transformation.

Whether the MSS fits a textbook definition, adopted by the DoW or derived from historical theory, is less important than its functional impact. If an RMA is indeed emerging or approaching, there should be tangible real-world consequences. Otherwise, theory becomes disconnected from practice. In this light, the MSS may serve as a bridge between the long-unfolding information RMA and a new, AI-driven transformation.

The MSS could be indicative of another significant shift in command and control (C2). While the US Army’s command post computing environment (CPCE) already integrates legacy systems into a modular, cloud-capable architecture for multi-domain operations, the MSS pushes these capabilities toward revolutionary real-time situational awareness.

While initially developed to automate drone feed analysis, the MSS has evolved into an AI-powered battlefield intelligence engine. It fuses intelligence, surveillance, and reconnaissance (ISR) data, enables real-time targeting, and supports distributed decision-making. As with the telegraph in the 19th century, the MSS may redefine the military’s relationship with information and time.

Historically, C2 was slow and fragmented. Commanders relied on flags, runners, and direct observation, limited by geography and transmission delay. The Industrial Revolution began to change this. Introduced in 1793, Claude Chappe invented the optical telegraph which allowed faster coordination across long distances. It was Samuel Morse’s electrical telegraph, patented in 1837, that truly revolutionized communication.

AI is reshaping combat just as electricity once did. Electricity transformed communication by creating the foundation for critical innovation, like the internet. The harnessing of electricity for industrial use itself was not an RMA, but it was the essential prerequisite for one. Without it, the revolution in communication that began with the telegraph would not have been possible. AI may not constitute a full RMA on its own, but it is the enabling foundation for one.

During the Crimean War and the American Civil War, the telegraph enabled real-time command for the first time. In the US, President Lincoln relied on the War Department telegraph office to direct Union forces and enforce strategic decisions. Strategic-level C2 became possible, and expectations for real-time situational awareness took hold. The rise of the steam-powered printing press and the expansion of railways accelerated this transformation, making war reporting nearly instantaneous—a precursor to modern information warfare.

Similarly, Project Maven, initiated in 2017, began as a machine learning initiative to automate drone video analysis. Since then, the MSS has grown to integrate cloud computing, ISR fusion, and targeting. The MSS delivers intelligence to the tactical edge at machine speed on enterprise cloud infrastructure. It processes unfathomable amounts of data in milliseconds— augmenting analysts and automating portions of the workflow.

Just like the electric telegraph centralized control and supported linear commander decisions, the MSS introduces machine learning, machine inference, and adaptive analytics to take command and control. The MSS provides a picture of the theater that is not merely quantitative, but qualitative.

A true RMA requires more than new technology. It demands operational adaptation, organizational restructuring, and doctrinal evolution. The MSS checks many of these boxes. Technologically, the MSS merges AI, edge computing, and cloud infrastructure in a holistic fashion. Operationally, it uses human-machine teaming to accelerate kill chains. Organizationally, it catalyzed the creation of institutions such as the Joint AI Center (JAIC) and the Chief Digital and Artificial Intelligence Office. Doctrinally, it promotes shifts toward algorithmic and mosaic warfare, which are adaptive, data-driven models of conflict.

The MSS could signal a broader shift in military operations, much like the telegraph reshaped communication in the 19th century. By combining intelligence, surveillance, and reconnaissance (ISR) with artificial intelligence at operational speed, the MSS is changing how armed forces interpret the battlespace, make decisions, and coordinate action—all while improving the shared situational picture. Yet without a corresponding cultural shift, even the best tools can fail to yield a true RMA. Whether the Department of War can fully adapt its doctrine and institutions to leverage the MSS remains to be seen.

Lieutenant Colonel Matthew J. Fecteau is an information operations officer working with artificial intelligence. The views expressed in this report are those of the author and do not necessarily reflect the official policy or position of the Department of the Army, the Department of War, or the US Government. 

Signals of a New Revolution: Maven Smart System and the AI-RMA Horizon was originally published on Global Security Review.

This means the cost is going to be exorbitant over using the cloud’s “in-place” structure of GPUs, distributed power etc.  The DOD procured and set up their own system, but where is the data coming from?

Air Force Launches Its Own Generative AI Chatbot. Experts See Promise and Challenges

The Air Force has entered into the ChatGPT game.  was originally published on Global Security Review.

Integrating AI/ML into low-observable technology presents a promising avenue for enhancing stealth capabilities, but it also comes with its own set of challenges. ML algorithms rely on large volumes of high-quality data for training and validation. Acquiring such data for low-observable technology is challenging due to the classified nature of military operations and the limited availability of real-world stealth measurements.

ML algorithms analyze vast amounts of radar data to identify patterns and anomalies that were previously undetectable. This includes the ability to track stealth aircraft and missiles with greater accuracy and speed. These advancements have significant implications for deterrence strategies as traditional stealth technology may diminish in its effectiveness as AI/ML-powered radar becomes more sophisticated, potentially undermining the deterrent value of stealth aircraft and missiles.

Stealth technology remains a cornerstone of deterrence, allowing military assets to operate relatively undetected. Radar, on the other hand, is the primary tool for detecting and tracking these assets. However, AI/ML are propelling both technologies into new frontiers. AI algorithms can now design and optimize stealth configurations that were previously impossible. This includes the development of adaptive camouflage that dynamically responds to changing environments, making detection even more challenging.

Furthermore, stealth technology encompasses a multitude of intricately designed principles and trade-offs, including radar cross-section (RCS) reduction, infrared signature management, and reduction of acoustic variables. Developing ML algorithms capable of comprehensively modeling and optimizing these complex interactions poses a significant challenge. Moreover, translating theoretical stealth concepts into practical design solutions that can be effectively learned by ML models requires specialized domain knowledge and expertise.

As ML-based stealth design techniques become more prevalent, adversaries may employ adversarial ML strategies to exploit vulnerabilities and circumvent the defenses afforded to stealth aircraft. Adversarial attacks involve deliberately perturbing input data to deceive ML models and undermine their performance. Mitigating these threats requires the development of robust countermeasures and adversarial training techniques to enhance the resilience of ML-based stealth systems.

Additional complexities are inherent in the fact that ML algorithms often operate as “black boxes,” making it challenging to interpret their decision-making processes and understand the underlying rationale behind their predictions. In the context of stealth technology, where design decisions have significant operational implications, the lack of interpretability and explainability poses a barrier to trust and acceptance. Ensuring transparency and interpretability in ML-based stealth design methodologies is essential for fostering confidence among stakeholders and facilitating informed decision-making.

Implementing ML algorithms for stealth optimization involves computationally intensive tasks, including data preprocessing, model training, and simulation-based optimization. As low-observable technology evolves to encompass increasingly sophisticated designs and multi-domain considerations, the computational demands of ML-based approaches may escalate exponentially. Balancing computational efficiency with modeling accuracy and scalability is essential for practical deployment in real-world military applications.

Integrating AI and ML into military systems raises complex regulatory and ethical considerations, particularly regarding autonomy, accountability, and compliance with international laws and conventions. Ensuring that ML-based stealth technologies adhere to ethical principles, respect human rights, and comply with legal frameworks governing armed conflict is paramount. Moreover, establishing transparent governance mechanisms and robust oversight frameworks is essential to addressing concerns related to the responsible use of AI in military applications.

Addressing these challenges requires a concerted interdisciplinary effort, bringing together expertise from diverse fields such as aerospace engineering, computer science, data science, and ethics. By overcoming these obstacles, AI/ML has the potential to revolutionize low-observable technology, enhancing the stealth capabilities of military aircraft and ensuring their effectiveness in an increasingly contested operational environment. On the other hand, AI/ML has the potential to significantly impact radar technology, posing challenges to conventional low-observable and stealth aircraft designs in the future.

AI/ML algorithms can enhance radar signal processing capabilities by improving target detection, tracking, and classification in cluttered environments. Analyzing complex radar returns and discerning subtle patterns indicative of stealth aircraft, these algorithms can mitigate the challenges posed by low-observable technology, making it more difficult for stealth aircraft to evade detection.

ML algorithms can optimize radar waveforms in real time based on environmental conditions, target characteristics, and mission objectives. Dynamically adjusting waveform parameters such as frequency, amplitude, and modulation, radar systems can exploit vulnerabilities in stealth designs—increasing the probability of detection. This adaptive approach enhances radar performance against evolving threats, including stealth aircraft with sophisticated countermeasures.

Cognitive radar systems leverage AI/ML techniques to autonomously adapt their operation and behavior in response to changing operational environments. These systems learn from past experiences, anticipate future scenarios, and optimize radar performance adaptively. Continuously evolving their tactics and strategies, cognitive radar systems can outmaneuver stealth aircraft and exploit weaknesses in their low-observable characteristics.

AI/ML facilitates the coordination and synchronization of multi-static and distributed radar networks, comprising diverse sensors deployed across different platforms and locations. By fusing information from multiple radar sources and exploiting the principles of spatial diversity, these networks can enhance target detection and localization capabilities. This collaborative approach enables radar systems to overcome the limitations of individual sensors and effectively detect stealth aircraft operating in contested environments.

ML techniques can be employed to develop countermeasures against stealth technology by identifying vulnerabilities and crafting effective detection strategies. By generating adversarial examples and training radar systems to recognize subtle cues indicative of stealth aircraft, researchers can develop robust detection algorithms capable of outperforming traditional radar techniques. ML provides a proactive defense mechanism against stealth threats, potentially rendering conventional low-observable technology obsolete.

AI and ML enable the construction of data-driven models and simulations that accurately capture the electromagnetic signatures and propagation phenomena associated with stealth aircraft. By leveraging large datasets comprising radar measurements, electromagnetic simulations, and physical modeling, researchers can develop comprehensive models of stealth characteristics and devise innovative counter-detection strategies. These data-driven approaches provide valuable insights into the vulnerabilities of stealth technology and inform the design of more effective radar systems.

In the quest for technological superiority in modern warfare, the integration of AI and ML into radar technology holds significant promise with the potential to challenge conventional low-observable and stealth aircraft designs by enhancing radar-detection capabilities. AI and ML algorithms improve radar signal processing, optimize radar waveforms in real time, and enable radar systems to autonomously adapt their operation. By leveraging multi-static and distributed radar networks and employing adversarial ML techniques, researchers can develop robust detection algorithms capable of outperforming traditional radar systems. Moreover, data-driven modeling and simulation provide insights into the vulnerabilities of stealth technology, informing the design of more effective radar systems.

The rapid advancement of AI/ML is revolutionizing both stealth and radar technologies, with profound implications for deterrence strategies. Traditionally, deterrence has relied on the balance of power and the credible threat of retaliation. However, the integration of AI/ML into these technologies is fundamentally altering the dynamics of detection, evasion, and response, thereby challenging the established tenets of deterrence. Of further concern is the consideration that non-stealth assets become increasingly vulnerable to detection and targeting as ML-powered radar systems become more prevalent. This could lead to a greater reliance on stealth technology, further accelerating the arms race.

This rapid development of AI/ML-powered technologies could destabilize the existing balance of power, leading to heightened tensions and miscalculations. The changing technological landscape may necessitate the development of new deterrence strategies that incorporate AI and ML. This could include a greater emphasis on cyber warfare and the development of counter-AI and counter-ML capabilities.

The integration of AI/ML into stealth and radar technologies will be a game-changer for deterrence. To maintain stability and prevent conflict, policymakers and military strategists must adapt to this new reality of a continuous arms race, wherein both offensive and defensive capabilities are constantly evolving in pursuit of technological superiority. Continued investment in AI/ML research is essential to stay ahead of the curve and maintain a credible deterrent posture. International cooperation on the development and use of AI/ML technologies in military applications is crucial to limit the scope of a potential arms race that regularly shifts the balance of power and destabilizes global security.

Joshua Thibert is a Contributing Senior Analyst at the National Institute for Deterrence Studies (NIDS) and doctoral candidate at Missouri State University. His extensive academic and practitioner experience spans strategic intelligence, multiple domains within defense and strategic studies, and critical infrastructure protection. The views expressed in this article are the author’s own

The Double-edged Sword of Artificial Intelligence was originally published on Global Security Review.

AI and the Future of Deterrence: Promises and Pitfalls was originally published on Global Security Review.