The growing gap between rapid advances in artificial intelligence (AI) and inadequate verification mechanisms has created several challenges for the technology. In this context, global AI governance has become a central topic of discussion in the tech world. However, the discourse around AI focuses primarily on how the technology will be regulated. The central problem of global AI governance is no longer whether AI should be regulated, but whether states possess the power to regulate the actors and infrastructures on which they increasingly depend.  

The international system has begun responding swiftly to evolving AI dynamics. Notably, the UN Global Digital Compact has established a framework for digital cooperation. The General Assembly has created the Independent International Scientific Panel on Artificial Intelligence, along with a Dialogue on AI Governance. In parallel, the European Union (EU) has moved towards an enforceable risk-based regulation through the AI Act. Likewise, the Council of Europe has also launched the first internationally binding treaty on AI. These initiatives are a step in the right direction. However, they lack a coherent global regime or direction, which impacts their effectiveness.  

As the latest initiatives show, the U.S. relies on targeted export controls on advanced chips, voluntary frameworks, and a pro-innovation approach that preserves U.S. dominance. China, on the other hand, favors a state-driven approach, built around controls on generative AI. The EU, by contrast, prioritizes enforceable rules for high-risk and general-purpose AI, along with strong fundamental rights protection. The emerging AI order is therefore not one model, but several operating simultaneously. This fragmentation is not accidental. It reflects competing power struggles among national interests. In such an environment, the search for a global AI governance appears misguided.  

Future AI governance is likely to mirror a hybrid model of international finance or trade regimes. Against this backdrop, private companies remain incredibly powerful, given their monopoly over data, models, computing power, and expertise, which are concentrated in a handful of private companies. This trend creates a governance paradox. Governments aim to regulate the private sector while remaining excessively dependent on it, creating a capacity challenge.  

These trends point to three prominent developments for the future. Regulatory fragmentation is likely to intensify as rival technological blocs establish incompatible standards. Ultimately, technologically backward states opt accordingly. Second is procedural multilateralism, where states agree on universal principles, but actual power remains concentrated among a few powerful entities. The third option is that states retain their distinct regulatory systems but agree on some conditions. These scenarios and trends are not mutually exclusive and can coexist, with different approaches emerging across sectors. As a result, the landscape is expected to remain complex. Whichever the scenario, the technological ecosystems are likely to become tools of influence vis-à-vis cloud access, advanced semiconductors, data centers, export policies, global supply chains, digital infrastructure, and divergent AI standards.  

For the Global South, the ongoing trend poses multifaceted challenges. The foremost is structural dependence. While these states may draft their own AI policies, they will remain dependent on foreign technology. Likewise, consensus among states remains elusive. This becomes increasingly complex in a future environment where AI governance may become more infrastructural. This might involve the development of measurable controls. The key challenge for the Global South is not mere exclusion; it is participation without agenda-setting influence. In addition, AI innovation moves much faster than regulation, creating a governance lag and adding to the challenges. The main priority is to define a meaningful role for itself in this environment. Its main priority should be to integrate into regional frameworks, shared datasets, and coordinated diplomatic moves.  

The international organizations have a limited but important part in global governance. They can expand their role by laying the ground for common scientific assessments, incident-sharing mechanisms, and capacity building. The approach should complement existing frameworks while acknowledging divergent national systems. Fostering coordination where political consensus allows remains central for relevant international organizations.  

AI Governance in the contemporary era is complex. As the ongoing developments suggest, the future of AI governance will not be manifested as a single regulator or a universal AI rulebook. Instead, it will emerge as a highly contested polycentric order, characterized by an environment where laws, norms, technical standards, and UN/international organization-led coordination overlap. In this context, the U.S., China, and the EU will continue to assert their dominance and institutionalize their edge into rules and norms. In parallel, countries in the Global South are likely to resist and consolidate their own influence. The pivotal challenge in such an environment will not be whether AI is governed. Instead, it will be whose security interests and priorities prevail in the technological age. Hence, the tussle will be to convert technological capability into institutional influence. 

Shaza Arif is a Senior Research Associate at the Centre for Aerospace and Security Studies (CASS), Islamabad, casss.thinkers@casstt.com. The views in this article are the author’s own and do not necessarily represent those of NIDS.

The Politics of Global AI Governance was originally published on Global Security Review.

The international system is undergoing a profound global power shift characterized by the resurgence of great power competition and a broad diffusion of technical capabilities. This environment is intensifying security competition across all domains. Concurrently, the proliferation of artificial intelligence (AI) and other disruptive technologies has fundamentally transformed espionage and defense. The traditional landscape of counterintelligence (CI) is obsolete and requires rapid, systemic overhaul to address the increasingly amplified, technologically enabled threats posed by state and non-state actors.

Specifically, the shift to great power technological competition has expanded CI’s mandate from protecting military secrets to securing critical infrastructure, intellectual property (IP), and the integrity of the information domain. The dual-use nature of AI functions as both in support of automated espionage and a critical mechanism for preemptively anticipating and mitigating threats. The failure of the United States to strategically integrate AI into CI methodologies will result in the systemic erosion of national technological and economic advantage.

The Expanded Mandate of Modern Counterintelligence

CI functions to protect a nation’s secrets, personnel, and systems from foreign intelligence entities (FIEs). Yet today, CI must also confront a threat matrix dramatically enlarged in scope, sophistication, and velocity. The current geopolitical climate has necessitated a significant expansion of the traditional CI mission. In the context of great power competition, the most significant threat has shifted from the theft of classified military and diplomatic secrets to the large-scale acquisition of IP, trade secrets, and technological data, as highlighted in the recently released Annual Threat Assessment.

FIEs are aggressively targeting the private sector, academia, and research institutions, the very engines of national innovation through sophisticated economic espionage. Their strategic goal is not merely to obtain information, but to erode a nation’s competitive advantage and accelerate the adversary’s technological timetable, thereby shifting the global balance of power. CI must establish robust protective mechanisms that extend deep into the non-governmental technology and research ecosystem.

The dissolution of a clear distinction between peacetime competition and active conflict has resulted in a continuous state of confrontation known as the ‘gray zone’. This strategic domain is characterized by persistent, non-lethal, yet tactically damaging activities designed to achieve political objectives without triggering traditional military responses. CI must now defend against a spectrum of subtle subversion, including large-scale cyber operations, persistent penetration of networks for reconnaissance and preparatory measures, and covert attempts to manipulate political discourse and decision-making.

The globalization of commerce and technology has created intricate, interconnected supply chains. These networks present significant CI risks, as adversaries seek to compromise the integrity, trustworthiness, and authenticity of products and services. By inserting “backdoors” or creating exploitable “choke points” at various nodes, adversaries establish capabilities for future exploitation. CI efforts are essential to conduct comprehensive due diligence and risk mitigation, securing these complex networks against both hardware and software compromise.

Artificial Intelligence: The Dual-Use Catalyst

AI and emerging technologies are not merely targets of modern espionage; they are simultaneously the most potent tools and the most necessary defenses in the counterintelligence battleground. This dual-use dynamic creates a challenging “AI vs. AI” scenario that demands immediate, radical adaptation. Adversaries are leveraging AI to dramatically enhance the speed, scale, and sophistication of their intelligence operations:

Automated Espionage and Big Data Analysis: AI-powered tools can automate and scale the processing, translation, and analysis of vast, heterogeneous datasets (Big Data), vastly increasing the volume and velocity of intelligence collection from both open-source intelligence and classified sources.

Adaptive Cyberattacks: Machine learning (ML) algorithms enable the development of more elusive and adaptive cyber threats. This includes automated exploitation of vulnerabilities, dynamic creation of polymorphic malware, and rapid penetration of defenses, operating at speeds that effectively outpace traditional, human-centric cybersecurity responses.

Generative AI for Influence: Generative AI can create highly realistic deepfakes (synthetic videos and audio) and synthetic narratives at scale. This facilitates sophisticated disinformation and propaganda campaigns to manipulate public opinion and conduct advanced social engineering, severely compromising the ability of institutions to discern truth from falsehood.

Three interconnected factors fundamentally redefine the scope of CI responsibility: target expansion, the blurring of conflict lines, and supply chain vulnerabilities. To effectively counter these technologically enabled threats, CI must aggressively embrace and integrate these same technologies, transforming them into proactive defensive tools:

Threat Anticipation and Predictive Analysis: AI can process and analyze massive amounts of threat data, identifying subtle, non-obvious patterns, trends, and anomalies. This capability allows CI to transition from merely reacting to threats toward predictive modeling, allowing one to forecast adversary actions before they materialize and enabling preemptive defense.

Enhanced Surveillance and Anomaly Detection: ML algorithms are crucial for the detection of subtle anomalies in network traffic, user behavior, and physical security systems that a human operator would miss. AI-driven monitoring provides real-time, large-scale pattern-of-life analysis that significantly exceeds human cognitive capacity.

Counter-Disinformation and Integrity Checks: CI requires AI-driven tools to effectively identify, analyze, and flag AI-generated propaganda, deepfakes, and synthetic media. Systems designed for content provenance and authenticity verification are essential to safeguard the integrity of the information domain and maintain public trust.

Insider Threat Mitigation: Defensively, AI can monitor internal networks to flag anomalous user behaviors such as unusual data access attempts, large data transfers, or deviations in an employee’s digital pattern-of-life. As such they assist in identifying potential insider threats before significant compromise occurs.

The Strategic Imperative

The shift of global powers and the proliferation of disruptive technologies have thrust counterintelligence into an even more important aspect of national security. The stakes of this technological arms race transcend traditional security concerns, encompassing the integrity of a nation’s innovative ecosystem, its economic competitiveness, and the resilience of its democratic institutions.

CI must rapidly evolve its strategies to prioritize the defense of economic and technological assets, and it must integrate AI as a foundational defensive technology to achieve predictive, scalable threat mitigation. Failure to aggressively master and deploy AI defenses against technologically augmented adversaries risks the systemic erosion of national advantage in a world where technological leadership is increasingly synonymous with global power. The future success of great power competition hinges directly on the adaptive capacity and technological sophistication of CI’s function.

Joshua Thibert is a Senior Analyst at the National Institute for Deterrence Studies (NIDS) with over 30 years of comprehensive expertise. His background encompasses roles as a former counterintelligence special agent within the Department of Defense and as a practitioner in compliance, security, and insider risk management in the private sector. His extensive academic and practitioner experience spans strategic intelligence, multiple domains within defense and strategic studies, and critical infrastructure protection. The views of the author are his own.

Redefining Espionage: The Unseen War for Technological Dominance was originally published on Global Security Review.

Focusing on information operations is critical. Drones, for example, use artificial intelligence capabilities, as do defensive systems. However, conflict between near-peer adversaries and competitors is still unlikely as gray zone and hybrid conflict are the dominant avenues for competition. With the information environment transcending all domains of warfare, artificial intelligence capabilities become the go-to capability to ensure and maintain information advantage.

China’s AI-enhanced information operations are becoming increasingly sophisticated. For example, the Chinese advanced persistent threat actor Spamouflage Dragon uses generative AI to create online personas to influence public opinion. China and its proxy companies seek to develop or compete for AI supremacy within the information environment.

Of course, China will use anything within its arsenal to shape strategic, operational, and tactical levels of war to its advantage, expand its influence, and create an ecosystem that is dependent on its technologies. For example, Baidu, known as the “Google of China,” invested billions into AI capabilities, creating the proprietary ERNIE model, which has been trained on billions of parameters, increasing the output’s quality and complexity.

However, China is also leveraging open-source AI models to shape the information environment. With the recent release of open-source large language models such as DeepSeek and Qwen, Chinese-linked subsidiaries, High-Flyer and Alibaba Group created a way to expand their influence, revise history, and likely create a dependent ecosystem for target countries. Unlike the much more expensive ChatGPT, for which the more basic model is free, China’s investment in generative AI models is free for the public and even surpasses ChatGPT’s in some respects.

There is a debate about how China’s proxy state companies were able to create these advanced models without US-based critical components. China allegedly did not have access to the advanced critical Nvidia chips for which most AI models are dependent. China seems to have created generative models just as suitable or even better than that of ChatGPT, but allegedly at a fraction of the cost and free of charge to the public. The US limited Nvidia chip exports to China, a market predicted to top $1 trillion in revenue within a decade. Still, the accusation is that the Chinese subsidiary leased or bought the more advanced Nvidia chips from Singapore, circumventing restrictions, and used ChatGPT to train its model.

Regardless of how China secured these critical technologies, the cat is indeed out of the bag. China has shown that it has the capability to develop new and emerging AI technologies. From the capabilities already built, it now has a baseline to create even more capabilities to develop its own AI chip ecosystem. With such capabilities, China will become more active within the information environment with the help of AI capabilities, and its motives are far from benevolent.

Why is the Chinese model free? China has several motives, but it is likely in hopes that data and information across the globe are the price tag for using the model while lessening a dependency on Western technologies and becoming a global leader in AI by 2030. Whatever data is obtained by the United States is icing on the cake. The West is not the primary target audience. Both models have servers in Singapore and China, where information is likely subject to Chinese laws, and terms and conditions are meaningless.

The Chinese will use AI technologies to gain an advantage in the information environment and seek to expand influence by creating an ecosystem for which other countries are dependent on their models. The incentive is to give countries this technology to foster dependency. The idea is similar to China’s debt-trap diplomacy—the Belt and Road Initiative. While ChatGPT’s basic model is free, China seeks to develop better models at a cheaper price to serve as leverage over countries that cannot afford the higher-end US-based models.

The United States is taking the right approach to maintaining its information advantage through AI development and investment. The billions pouring into creating AI data centers will play an important role in ensuring the United States has the edge in AI.

These data centers remain critical for identifying and countering any malign information operations against the United States, its partners, and its allies. When Iran attempted to influence the 2024 presidential election using the generative model GPT, OpenAI detected and shut it down. Without this expansive investment in AI data centers that keep information within the letter of US law and oversight, these interventions would be out of reach, and information operations may be even more challenging to detect.

However, this approach is insufficient without incorporating artificial Intelligence into all aspects of military operations. The DoD uses artificial intelligence within some branches, but given the expansive nature of AI, this is not enough. AI is expected to touch nearly all aspects of military operations, especially information operations, and may not have time to wait for its major AI initiative, Project Maven, to fully develop.

Some military scholars have called something like this a revolution in military affairs, but perhaps, given the impact of war, it could be classified as such. The concept is somewhat antiquated and outdated without some context, but it remains the best way to describe what should take place within the DoD. The foundation is already in place through the conceptual framework of multidomain operations.

Artificial capabilities are widely available through graphical user interfaces in deployable, ready-to-use form, such as ChatGPT or even internal large language models. The joint force should use these capabilities to the broadest extent possible. If anything, artificial intelligence, including large language models, will make joint and combined forces more lethal and accurate as they counter Chinese efforts within the information environment.

The DoD must adopt incentives for service members to understand the capabilities of AI and incorporate them in all training environments. These incentives can include bonuses for taking AI-driven courses. The DoD can also increase awareness and accessibility of AI courses on its education platforms which now have a paucity of artificial intelligence courses.

The DoD must also improve the training environment. With proprietary or off-the-shelf software, the DoD can incorporate AI offensive and defensive platforms within all training and mission-critical tasks. Even simply assisting with identifying generative outputs, e.g., deepfakes, will counter Chinese influence within the information environment, especially during hybrid conflict. Furthermore, military doctrine should recognize the importance of AI, especially information operations, with an emphasis on psychological operations.

While AI investment is critical to countering Chinese influence within the information environment, the only way to truly embrace multidomain operations is to ensure service members have the AI technical competency necessary to maneuver within the information environment deterring Chinese aggression.

US Army Lieutenant Colonel Matthew J. Fecteau is a PhD researcher at King’s College London studying how artificial Intelligence will impact conflict. He can be reached at matthew.fecteau.alumni@armywarcollege.edu.

China’s AI-Driven Information Operations Are Here: The US Needs an AI RMA was originally published on Global Security Review.

Missile flight time between both India and Pakistan is a few minutes. Hence, commanders do not have time for verification and decide to launch a retaliatory strike on warning.

In Pakistan, a high-ranking Pakistani military officer contacts Pakistan’s prime minister to tell him India has initiated a nuclear strike, and an immediate response is required. The prime minister immediately orders a counterstrike. Pakistan proceeds to counterstrike protocols.

Missiles begin crossing borders between India and Pakistan as the world hurtles towards a nuclear catastrophe in South Asia. The world’s worst fears are realized. It is only later realized that the high-ranking Indian military officer’s message to an Indian commander was made by a nuclear command-and-control system embedded with generative artificial intelligence (AI).

The Role of Generative AI in Modern Warfare

The use of generative AI, which is best exemplified by ChatGPT, by an adversary, or third party, can impact deterrence stability between India and Pakistan. In a worst-case scenario, it could trigger a nuclear war, like the scene mentioned above. Should generative AI be embedded in nuclear command-and-control systems, it is theoretically possible that hyper-personalized messages, like the one described above, could be generated in a system that was not originally designed to allow such an outcome. The potential for a tailored message, for specific individuals, is possible.

Traditionally, content and information generation were based on human creativity. However, at present, generative artificial intelligence is being considered as a tool for digital transformation. Generative AI is a deep learning model which has the capability to generate synthetic media including text, video, and audio—dependent on training.

Perception Building and the Escalation of Crises

Generative AI can also play a distinctly different role in leading to nuclear crisis. In a deterrence framework, the development of a narrative and perception are crucial. Similarly, communication of deterrence threats is also achieved through perception-building of an adversary—concerning a state’s capabilities and credibility. In the specific case of India and Pakistan, India is creating misperceptions regarding Pakistan’s nuclear program. Recently, Pakistani Ambassador Munir Akram, at the United Nations Security Council’s high-level debate, stated that India is actively pursuing a state-led disinformation campaign to malign Pakistan.

He was referring to recent EU Disinfolab’s reports on how India is using fake nongovernmental organizations, think tanks, media outlets, and international organizations to malign Pakistan. Similarly, it is possible that India may use Generative AI to manipulate public opinion, thereby, creating misperceptions among Pakistan’s public. This could make the citizenry, as well as the government of both India and Pakistan, susceptible to separate facts from fiction—especially in a crisis situation. Hence, AI-driven narratives could push both countries to a crisis leading to warfighting.

Misinformation and Crisis Management Challenges in South Asia

Misinformation through generative AI can make crisis management and escalation dominance between India and Pakistan much more difficult. According to Heather Williams and Alexi Drew, Herman Kahn’s escalation ladder has transformed into a web across domains with multiple actors and alliances, especially in the age of social media. In South Asia, due to mated warheads and cannasterization of missiles by India, coupled with geographical contiguity with Pakistan, nuclear use may become ultimate escalation within minutes—without a ladder or a web. Social media could be a threat multiplier and can become lethal with the use of generative AI. Although, tweets are unlikely to independently start a crisis, tweets from government officials of India and Pakistan could be fabricated through utilizing hacking and generative AI to escalate an ongoing crisis.

Risks of Generative AI in Command-and-Control Systems

While Indian nuclear weapons may have an effective command and control where nuclear weapons have nuclear safety and security mechanisms, there was a “Brahmos missile crisis” in 2022. Inadvertent or accidental, the launch of a missile could trigger a reactionary strike by Pakistan as the missile is dual-capable. Furthermore, this fog of war could undermine strategic stability. As a Group Captain was blamed for the launch, many commanders like him, who may be capable of launching nuclear missiles, even on Indian nuclear submarines, could be manipulated via high-level orders created through generative AI.

False Flag Operations and Strategic Escalation

India has also opted for adventurism against Pakistan through its false flag operations and surgical strikes. Pakistan has adopted a conventional strategy of quid-pro-quo plus as a conventional response to such Indian limited strike. However, malicious false flag operations through disinformation and fake news by India to garner public support for electoral gains and domestic politics can result in crisis escalation in such a competitive strategic environment. It is due to mistrust within the adversarial relationship of India and Pakistan.

The Need for Confidence-Building Measures (CBM) in AI

There is a dire need for risk reduction and confidence-building measures (CBM) between India and Pakistan in the domain of AI. However, the appetite for CBMs and dialogue on the Indian side is non-existent. India and Pakistan could establish bilateral AI incident reporting and mitigation centers to counter the use of AI as a tool for inducing false alarms and other escalatory actions. Furthermore, there is a need for India and Pakistan to further opt for unilateral AI security and regulation measures, especially for inter-organizational and inter-state strategic communications. If leading states like the United States, China, and Russia opt for an AI arms control agreement, less powerful states, including India and Pakistan, may be persuaded to follow suit. This will help establish a universal AI arms control regime regarding the use of AI for military purposes.

Generative AI and Deterrence Stability: A Scenario for De-escalation

It is the night of May 28, 2028, at India’s military command center. A high-ranking Indian military officer sends a message to a subordinate commander; Pakistan has initiated a preemptive strike. Prepare for a retaliatory strike. This is not a drill.

Flight time between India and Pakistan is a few minutes. Hence, commanders do not have time for verification and need to decide quickly whether to launch a retaliatory strike. He calls the command center to verify the message received.

India’s prime minister is contacted immediately concerning the message received by one of his commanders. He picks up the hotline to Pakistan’s prime minister and asks, “Have you launched nuclear weapons?”

Pakistan’s prime minister responds, “Not at all. This is fake!”

India’s prime minister informs the military command center. The crisis is averted, and nuclear weapons are not used.

Confidence-building measures, which were present in the second scenario, have the opportunity to change the outcome of a conflict. Given the potential for artificial intelligence to be used in nefarious ways, it is time to take action to avert such a crisis.

Ms. Anum A. Khan is an Associate Director at the Center for International Strategic Studies (CISS) Islamabad. She is also currently a Research Fellow at Comprehensive Test Ban Treaty Organization and a Project Associate of The Third Nuclear Age Project led by Prof. Andrew Futter.

Generative Artificial Intelligence and Deterrence Stability between India and Pakistan was originally published on Global Security Review.