The Geopolitics of AI-Enabled Cyber Conflict: What Security Leaders Need to Understand Now

The offense-defense balance in cybersecurity is shifting, with AI as the lever and geopolitics as the fulcrum. This article, which expands on insights from a recent episode of The Cyber Circuit podcast, goes in-depth into the fundamentals that security leaders should know about this trend.

For years, cybersecurity professionals have operated under a stable, if uncomfortable, asymmetry: defenders must succeed continuously, while attackers need only succeed once. That asymmetry has been partially offset by AI-driven tools that scale defensive coverage at low cost across large attack surfaces. But the assumption that AI will continue to favor defenders deserves scrutiny, and the geopolitical forces shaping who controls these tools deserve far more attention than they currently get in enterprise security strategy.

The CNAS report “Tipping the Scales: Emerging AI Capabilities and the Cyber Offense-Defense Balance,” authored by Caleb Withers, is one of the more serious attempts to map this terrain. It served as the basis for a recent episode of The Cyber Circuit, on the Insight Jam channel, which is worth watching alongside this piece for additional context and commentary from CSO practitioners.

Why the Defender’s Advantage May Be More Fragile Than It Looks

The current consensus, backed by observable market dynamics, is that AI has been a net positive for cyber defense. Automated threat detection, behavioral anomaly analysis, and AI-assisted vulnerability scanning have enabled security teams to expand their effective coverage without proportional increases in headcount. This is real and meaningful.

The fragility lies in three underappreciated trends.

The first is kill-chain compression. Historically, sophisticated cyber intrusions required substantial human labor in the planning and reconnaissance phases, often hundreds of person-hours over weeks or months. That lag time has served defenders implicitly: a vulnerability disclosed today rarely gets exploited by a sophisticated actor the same day. As agentic AI systems improve, the automated orchestration of full attack sequences from target identification through exploitation and exfiltration becomes increasingly plausible. If that planning-to-exploitation window collapses from days to hours, the economics of patch deployment change fundamentally.

The second is compute economics. Most defensive AI tools today benefit from near-zero marginal cost deployment once a model is trained. That calculus begins to shift as the most capable frontier models require significant compute expenditure per inference. Defenders with broad attack surfaces may find that uniformly applying state-of-the-art AI coverage is cost-prohibitive. At the same time, well-resourced offensive actors, including nation-states with dedicated budgets, can allocate substantial compute to high-value targets.

The third is reliability asymmetry. An attacker operating with an unreliable AI tool absorbs the downside as wasted effort or discovery risk. A defender who has integrated an unreliable AI system into critical infrastructure absorbs the downside as operational failure. The CrowdStrike outage of July 2024 illustrated this dynamic starkly: a software update failure cascaded into one of the most disruptive single-day IT incidents in recent history. The lesson for AI integration is not that defenders should avoid AI, but that the reliability bar for defensive AI must be substantially higher than for offensive AI. That threshold is harder and more expensive to meet.

The Secure-by-Design Baseline Problem

One of the CNAS report’s core policy recommendations is strengthening secure-by-design and secure-by-default requirements across government procurement and, by extension, critical infrastructure. The argument is straightforward and important: as AI lowers the cost of scanning for basic, unsophisticated vulnerabilities at scale, organizations that have coasted on security-by-obscurity will face increasing exposure. The window of benign neglect, in which a system has not yet been targeted, is closing.

The governance mechanism that would actually move the needle here is not awareness campaigns. It is the procurement standards and proportionate liability. Federal acquisition regulations are slowly moving in this direction, with zero-trust architecture adoption requirements gaining ground in DoD procurement, though the timeline remains measured in years rather than months. On the liability side, sector-specific regulation in finance and healthcare provides a rough template, but critical infrastructure sectors like water utilities operate with significantly weaker accountability structures.

Like Michael Morgenstern and Caleb Withers discuss in their episode of The Cyber Circuit, the politically uncomfortable implication is that meaningful secure-by-design adoption will require some form of consequence for negligence, not just incentives for compliance. With AI-assisted attack scanning accelerating, the legal and regulatory tolerance for avoidable vulnerabilities should decrease in proportion.

Export Controls, Compute, and the China Problem

Intellectual property theft from AI developers is a genuine concern, but it is also partially a distraction from a more tractable lever: compute access. Information flows are difficult to control precisely because the information ecosystem that produces AI progress is deliberately open, and that openness is a feature, not a bug. Attempting to lock down research communications would impose enormous costs on US competitiveness in exchange for uncertain and likely modest security gains.

Compute, or computational power, is more controllable, and export controls on advanced semiconductors have demonstrably affected the pace of frontier model development in China. DeepSeek’s published research has explicitly acknowledged constraints on the scale of training runs as a limiting factor. This does not mean export controls are a permanent solution. China is investing heavily in domestic semiconductor development, and extreme ultraviolet lithography capabilities remain a key chokepoint that is genuinely difficult to replicate on compressed timelines.

The strategic conclusion is that compute controls buy time, not a permanent advantage. The policy question is what the US and allied governments do with that time to build durable advantages in model quality, evaluation infrastructure, and defensive application.

The Alignment Problem in National Security Contexts

The alignment challenge for general-purpose AI systems, ensuring models behave as intended across diverse and adversarial conditions, takes on a different character in national security applications. A commercial AI model optimized to be helpful, honest, and harmless is not the same as a model being deployed for cyber offense operations, where “harmless” is explicitly not the objective.

This creates an underappreciated governance gap. Procurement processes for AI in national security contexts need to assess not just what a model can do under ideal conditions, but how it behaves under adversarial conditions, in edge cases, and when operating with extended autonomy. The evaluation infrastructure for this kind of testing is underdeveloped relative to the ambitions for deployment.

As AI autonomy in national security applications increases, we can assume that the risk of consequential model behavior that diverges from operator intent will also increase. This is not a science fiction concern about rogue AI, but a near-term operational reliability concern about systems making high-stakes decisions faster than human oversight can meaningfully intervene.

The UK AI Security Institute has offered a model worth noting here. By investing in genuine technical evaluation work, including benchmark development and published research, the institute has created conditions where frontier AI developers actively want to collaborate with it. That is a more durable form of influence than regulatory mandates alone could achieve, and it is a template that middle powers and allies with limited compute budgets can realistically pursue.

What the Geopolitical Structure Actually Implies for Security Teams

The US-China AI competition is the dominant structural fact, but security leaders in enterprise contexts should resist treating it as background noise. A few implications are practical and immediate.

The provenance of AI tooling in the security stack deserves scrutiny, unlike conventional software, which has not historically received it. Models trained on data or fine-tuned with infrastructure that has unclear provenance carry supply chain risks that are difficult to assess and easy to underweight.

The talent hollowing-out risk is real and underappreciated. As AI handles more routine detection and response tasks, organizations face a slow erosion of the human expertise needed to respond effectively when AI systems fail or are actively subverted. Maintaining surge capacity in human cyber expertise is not just workforce planning; it is a resilience strategy.

Finally, the liability and regulatory landscape will change. Organizations that have not begun aligning their security posture with emerging federal standards are betting that the enforcement timeline is long. Based on current legislative and regulatory momentum, that bet carries more risk than it did three years ago.

The fundamental insight from both the CNAS research and practitioner conversations, such as those on The Cyber Circuit, is that the offense-defense balance in AI-enabled cyber conflict is not fixed. Investment decisions, procurement standards, export policy, and evaluation infrastructure actively shape it. Security leaders who treat these as someone else’s problem are ceding influence over the environment in which they will have to operate.

FAQ

Q: Is AI currently favoring cyber attackers or defenders?

A: The present balance still modestly favors defenders, primarily because AI-driven detection and scanning tools scale defensively at low marginal cost. However, several trends, including kill-chain automation, compute economics, and reliability asymmetry, could shift this balance toward the offense within a 3- to 7-year window.

Q: What are export controls on semiconductors actually accomplishing?

A: They are constraining the scale of training runs that Chinese frontier AI developers can execute, which affects both model capability ceilings and the economics of running models at scale for offensive applications. They are buying time, not a permanent advantage.

Q: Should AI companies like Anthropic be liable when their models are used by nation-state hackers?

A: No, and this is not a serious near-term policy proposal. The more tractable question is what baseline monitoring and usage controls are proportionate to require, without chilling the defensive applications of the same dual-use technology.

Q: What can smaller countries or mid-size enterprises actually do in an AI arms race they cannot win on compute?

A: Investment in evaluation infrastructure and benchmark development offers outsized leverage. Countries and organizations that develop genuine technical credibility in testing and assessing AI capabilities gain influence with frontier developers that compute investment alone cannot buy.

Q: What is the alignment risk specific to national security AI deployments?

A: Models aligned to general consumer values (helpful, honest, harmless) are not aligned to the operational requirements of national security contexts, which may require deception, harm, and strategic autonomy as features rather than bugs. Building evaluation frameworks for this divergence is an underinvested problem.