AI and National Security: Risks, Governance, and Strategy

Emerging technologies are reshaping global power, economic growth, and the risks that governments need to manage. Among them, artificial intelligence (AI) has become a central concern for national security planners, both because of its potential to enhance military and intelligence capabilities and because of the difficulty of governing fast-moving, widely diffused software tools.¹

This article draws on debates in security and technology policy to explain why AI matters for global stability, what kinds of institutions and tools can help manage its risks, and how governments can respond without either overreacting or falling dangerously behind.

From Niche Topic to Security Priority

Until recently, AI was mainly seen as a research field and commercial opportunity. Over the last decade, it has moved into the core of national security discussions in the US, China, and other technologically advanced states.¹²

• Technological progress: Breakthroughs in deep learning, large-scale computing, and data availability have led to systems that can classify images, translate languages, and generate text at or above human level in some tasks.³
• Strategic implications: Defense planners now treat AI as a potential “enabling technology” that can improve logistics, sensing, decision support, and cyber operations, rather than just as another software tool.³
• Political visibility: High-profile applications, from autonomous weapons debates at the UN to large language models used by millions of people, have forced policymakers to develop positions on AI faster than they usually would for a complex technology.

As a result, new institutions have emerged to bridge the gap between technical research and policy, including analytic groups embedded in universities, think tanks, and government agencies that focus specifically on “security and emerging technology.”¹

Why Emerging Technologies Challenge Traditional Security Thinking

Emerging technologies differ from many older defense capabilities in ways that make them hard to regulate and predict.

Because of these differences, policymakers need to adjust their playbook. Tools that worked for nuclear weapons or heavy conventional arms—such as physical inspections, fixed-site export controls, and treaty-based limits on stockpiles—translate poorly to rapidly changing software and algorithms.³

AI as an Enabling Capability, Not a Single Weapon

AI is better understood as a set of techniques for making predictions and pattern recognitions than as a single discrete capability. One useful analogy is to think of modern machine learning as a very powerful, data-driven extension of statistics that can be embedded into many different systems.

This has two important consequences for security strategy:

• Diffuse impact: The same underlying methods can power recommendation systems, autonomous drones, fraud detection, and battlefield logistics optimization. There is no single “AI system” to regulate.
• Context-dependent risk: An image classifier deployed in a medical setting has different failure consequences than one used in targeting or surveillance. Risk stems from use context, not just model capability.

For policymakers, this means it is rarely helpful to talk about “controlling AI” in the abstract. It is more tractable to focus on:

• Specific applications (e.g., autonomous weapons, predictive policing, critical infrastructure management)
• Certain classes of “frontier” models that require extraordinary computational resources and pose exceptional systemic risks
• Shared safety practices across sectors, such as red-teaming, evaluation, and incident reporting

Institutions That Translate Between Research and Policy

One of the most practical responses to emerging technology risk has been the creation of specialized research groups focused on security and technology. These organizations aim to make highly technical developments legible to non-technical decision-makers in a way that informs law, regulation, and strategic planning.¹

Effective analytic institutions in this space tend to:

• Recruit hybrid talent: People who can read technical papers and also understand legislative processes, budgeting, and bureaucratic incentives.
• Produce policy-relevant work: Short, timely analyses and memos that answer the specific questions policymakers actually have, rather than only broad theoretical reports.
• Maintain independence: Sufficient distance from industry and government to offer candid analysis, but enough engagement to be taken seriously and consulted at key decision points.

Such groups often sit at universities or independent think tanks, sometimes with philanthropic seed funding and later with a mix of public and private support. Their role becomes more central as technology questions move from niche committees to the core of national security decision-making.

How Governments Can Shape the Trajectory of AI

Despite the speed of private-sector innovation, governments still have powerful levers over how AI is developed and deployed.³

Key Policy Levers

• Public research funding: Grants and university programs steer talent and research toward safety, robustness, and socially beneficial applications.
• Compute and data governance: Rules around access to advanced chips and large-scale cloud resources can slow or shape the development of the most capable models, while standards for data collection and protection affect which applications are viable.
• Procurement policy: Government agencies are among the largest technology buyers. Requiring transparency, testing, and risk assessments in procurement contracts can shift industry norms.
• Regulation and liability: Sector-specific rules—for example in finance, healthcare, or critical infrastructure—can require risk management for AI systems without regulating the entire field at once.

Opportunities in Digital Infrastructure

One underappreciated way that AI affects security is through changes in how governments handle digital infrastructure. Large-scale cloud contracts, standardized data pipelines, and interoperable systems can dramatically change the state’s capacity to use information, even before cutting-edge models are deployed.

In practice, this may mean that:

• The biggest long-term gains come from modernizing data infrastructure rather than trial deployments of flashy AI tools.
• Security risks also increase if data is centralized without strong access controls, monitoring, and response capabilities.

US–China Competition and AI

AI has become a focal point in broader US–China strategic competition. Both countries see leadership in advanced computing as key to economic and military power, though their approaches and constraints differ.¹⁵

Areas of Competition

• Semiconductors and compute: The US and its allies currently lead in advanced chip design and manufacturing tools, while China has invested heavily to reduce dependence on foreign suppliers.³⁰
• Standards and governance: Both sides attempt to shape international technical standards and norms surrounding AI, data flows, and surveillance technologies, which can lock in commercial advantages and influence global practices.
• Talent and research ecosystems: Universities, open-source communities, and corporate labs are globally integrated, but geopolitical tensions have made cross-border collaboration more sensitive.

Risks of Viewing AI Only Through a Rivalry Lens

Framing AI purely as a race between nations can obscure shared risks and opportunities. Many of the most serious dangers—such as misuse of powerful models for cyberattacks, biological threats, or destabilizing disinformation—are cross-border issues where states have overlapping incentives to cooperate.³

Balanced policy needs to support:

• Targeted restrictions where security is clearly at stake (e.g., advanced chips for military uses)
• Continued scientific openness in areas where collaboration reduces global risk (e.g., safety evaluation methods, incident reporting practices)
• Mechanisms for crisis communication and norms around dangerous deployments, similar to past efforts in nuclear and cyber domains

Managing AI Risks: From Accidents to Misuse

Policymakers and researchers distinguish between at least three broad categories of AI risk:

• Unintentional failures: Systems that behave unpredictably, fail under unusual conditions, or generalize in harmful ways because they were trained on incomplete or biased data.¹
• Malicious misuse: Actors who deliberately use models to conduct phishing campaigns, generate persuasive propaganda, or help design harmful biological or cyber tools.³
• Systemic risks: The possibility that widely deployed, interconnected AI systems introduce new failure modes or feedback loops in finance, infrastructure, or conflict escalation.

Each category points to different interventions:

• Technical safety research and standards for testing, monitoring, and evaluation to address unintentional failures.
• Access controls, auditing, and international cooperation on law enforcement to reduce malicious misuse.
• Macro-level oversight of critical sectors, including stress-testing and contingency plans, to mitigate systemic risks.

The Role of Law, Norms, and Soft Governance

Formal regulation is not the only way societies shape the behavior of powerful technologies. Soft governance—non-binding principles, industry norms, and professional standards—can often move faster and adapt more easily than statutes.

Recent years have seen a proliferation of AI-related governance tools, including:

• Voluntary safety commitments by major AI developers in some jurisdictions
• Technical benchmarks and evaluation frameworks developed by national standards bodies and research institutions³
• Professional codes of ethics for engineers and data scientists, especially in sensitive fields such as healthcare and policing

These mechanisms do not replace legal authority, but they can prepare the ground for more targeted, enforceable rules by testing concepts, building shared vocabulary, and highlighting implementation challenges early.

What Policymakers Need from Technical Experts

Technical experts often underestimate how difficult it is for busy officials to interpret cutting-edge research in practical terms. Effective communication between communities is as important as raw technical sophistication.

Policy teams typically benefit most from experts who can provide:

• Clear threat models: Concrete stories about how a technology might be used or fail, including plausible timelines.
• Decision-relevant metrics: Ways of measuring model capability, robustness, or proliferation that are legible to non-specialists.
• Actionable options: Specific, realistically implementable policy steps, with trade-offs and uncertainties spelled out.

For technologists interested in supporting better policy, this often means learning enough about law and bureaucracy to understand what kinds of recommendations are feasible and when they are likely to be considered.

Careers at the Intersection of Security and Emerging Technology

The growing importance of emerging technology has created demand for people who can operate at the boundary between research, policy, and implementation. These roles span government, academia, think tanks, industry, and civil society.

Examples of High-Impact Paths

• Government analyst or advisor: Working in defense, foreign affairs, or legislative offices to integrate technical understanding into national security and economic policy.
• Researcher in technology policy: Conducting empirical, legal, or strategic analysis of how AI and other technologies affect stability, rights, and governance.
• Standards and safety specialist: Helping to design evaluation frameworks, benchmarks, and best practices for secure and reliable AI deployment.
• International governance professional: Engaging with multilateral bodies on norms and agreements for responsible technology use.

Because the field is relatively new, diverse backgrounds—science and engineering, law, economics, area studies, or public policy—can all be valuable, especially when combined with a willingness to learn across disciplines.

Frequently Asked Questions (FAQs)

Q1: Is AI really transformative for national security, or is it mostly hype?

AI is both overhyped and underappreciated. It is unlikely to replace traditional military power on its own, but as an enabling technology it can significantly enhance sensing, logistics, planning, and cyber capabilities. Many of the most meaningful changes may be gradual and infrastructural rather than dramatic single-weapon breakthroughs.³

Q2: Can governments realistically control the spread of powerful AI models?

Complete control is unrealistic, especially for widely available techniques. However, governments can influence the development and deployment of the most advanced models through controls on specialized chips, large-scale cloud computing, and corporate practices. The goal is not to stop all diffusion, but to slow and shape the riskiest developments and uses.³

Q3: How should policymakers balance innovation with security concerns?

A balanced approach distinguishes between low-risk applications that can be lightly regulated and high-stakes uses that demand rigorous testing, transparency, and oversight. Rather than blanket rules on “AI,” targeted regulation by sector and risk level allows beneficial innovation while imposing safeguards where failures would be most harmful.

Q4: Is international cooperation on AI governance realistic given geopolitical tensions?

Full alignment of interests is unlikely, but limited cooperation on shared risks—such as avoiding accidental escalation, reducing cyber vulnerabilities, or restricting certain dangerous applications—is both possible and historically precedented. Even during periods of intense rivalry, states have agreed on guardrails for nuclear and cyber activities, and AI can follow a similar path.³

Q5: What skills are most useful for a career in security and emerging technology?

Useful skill sets include a solid grounding in at least one technical or analytical field (such as computer science, statistics, or economics), strong written communication, and an understanding of how institutions like legislatures, ministries, and international organizations make decisions. Cross-cultural competence and knowledge of key regions, especially major technology powers, are increasingly important.¹⁵

Written by

Sneha Tete

• Integrated M.A. in Applied Linguistics, University of Hyderabad
• 5+ years in editorial, instructional, SEO, and research-based writing

Sneha is a relationships and lifestyle writer with a strong foundation in applied linguistics and certified training in relationship coaching. She brings over five years of writing experience to theskillvault,  crafting thoughtful, research-driven content that empowers readers to build healthier relationships, boost emotional well-being, and embrace holistic living. Her writing and insights reflect the belief that beauty, happiness, and wellness…