AI Gray Zone Rules: Boundaries Before Escalation

A lot of national security AI talk still orbits the battlefield: autonomous targeting, faster kill chains, smarter command and control. That framing misses where AI already bites hardest—the gray zone, where states compete below the threshold of armed conflict.

Here’s the practical problem: AI is portable. The same model architecture that flags a missile launch pattern can be adapted to micro-target voters, pressure markets, or automate coercive diplomacy. When tools move that easily across missions, it’s not just a technology issue—it’s a governance issue. And if governance lags, escalation stops being a deliberate choice and starts becoming an emergent property of a system.

This post is part of our “AI in Defense & National Security” series, focused on surveillance, intelligence, mission planning, autonomous systems, and cybersecurity. The goal here is simple: give you a usable way to draw operational and ethical boundaries for AI in ambiguous competition—before competitors (or incidents) draw them for you.

The gray zone is where AI causes the most strategic trouble

Answer first: The gray zone is where AI can quietly reshape perception, incentives, and decision cycles—often without obvious attribution—making miscalculation more likely than in conventional combat.

Gray zone operations aren’t new: influence campaigns, cyber disruption, economic coercion, and persistent intelligence collection have been standard features of statecraft for decades. What’s changed is speed and scale. AI enables:

• Industrialized influence operations (content generation, persona management, audience targeting)
• Faster intelligence-to-action loops (automated collection triage, fusion, anomaly detection)
• Cheaper disruption (automated vulnerability discovery, social engineering at scale)
• More persuasive coercion (personalized messaging, behavioral prediction, tailored intimidation)

The strategic danger isn’t only that adversaries will do this. It’s that when everyone can do it cheaply, gray zone competition becomes noisier and more aggressive, and leaders get less time and less clarity to interpret what’s happening.

Why “responsible AI” breaks down below the threshold of war

Most existing “responsible AI” language is built for discrete events: a strike, an intercept, a targeting decision. Gray zone operations are different:

• They’re persistent, not episodic.
• They’re multi-domain (information, economic, diplomatic, cyber), not single-channel.
• They blur foreign vs. domestic spillover.
• They rely on deniability and indirect effects.

That combination is why AI governance can’t just be a model card and a compliance checklist. In gray zone competition, you need mission boundaries that travel with the capability.

Borrow a better mental model: “separate the spheres” of statecraft

Answer first: A workable policy line starts by treating diplomacy, information, military, and economic competition as distinct “spheres” with different rules—then limiting which AI capabilities are allowed to migrate across them.

Political theorist Michael Walzer argued that societies stay just (and stable) when they keep different spheres—like politics and markets—governed by their own norms. Apply that to national security AI and you get a blunt but useful insight:

AI becomes most dangerous when a capability built for one sphere is repurposed in another without new constraints.

That “portability problem” is the hidden accelerant in modern competition. A model trained to detect deception can also manufacture it. An optimization system built to manage logistics can also optimize coercion. Without clear constraints, you don’t have “AI supporting policy.” You have AI expanding what’s thinkable.

What “separate spheres” looks like across DIME

Most U.S. national security professionals already think in DIME: Diplomatic, Informational, Military, Economic instruments of power. The Walzer-style update is to attach each sphere to a governing logic and design AI guardrails around it.

• Diplomatic sphere (persuasion with agency): AI can support negotiation prep, translation, scenario analysis, and situational awareness. It crosses a line when it becomes automated manipulation—deepfake simulation of counterparts, exploitation of psychological vulnerabilities, or coercive “choice architecture.”

• Information sphere (truth, accountability, cognitive autonomy): AI can help detect interference, triage intel, and identify coordinated inauthentic behavior. It becomes corrosive when used to distort reality—synthetic personas, automated propaganda, or influence operations designed to alter political behavior without consent.

• Military sphere (necessity and proportionality): AI can improve force protection, reduce collateral damage through better discrimination, and speed battlefield management. It loses legitimacy when repurposed for domestic control or when autonomy expands engagements by default.

• Economic sphere (fairness, consent, transparency): AI can strengthen sanctions enforcement and illicit finance detection. It undermines rule-bound exchange when used for opaque market manipulation or algorithmic coercion of firms and populations.

The point isn’t to create a moral philosophy seminar. It’s to ensure mission planning and system engineering start from the same boundary assumptions.

A practical framework: Prohibited, Restricted, Permissive

Answer first: For gray zone AI policy to work, you need a three-tier decision structure—what you will not do, what you will do only under tight controls, and what you should actively build and deploy.

This is the part most organizations skip. They write principles, but they don’t translate them into procurement requirements, authorization processes, or test plans. A three-tier framework forces that translation.

Prohibited: AI uses that destroy legitimacy and invite blowback

Prohibited means the mission is off-limits, even if it’s tactically effective.

In gray zone competition, the U.S. advantage often depends on allied trust, intelligence sharing, and credibility. Some AI-enabled behaviors torch that advantage fast—and create symmetrical retaliation incentives.

Examples that belong in the prohibited bucket:

• Large-scale AI deception campaigns using deepfakes, synthetic personas, and automated propaganda
• AI-enabled interference in democratic processes, including micro-targeting intended to manipulate voting behavior
• AI-enabled surveillance of civilians outside conflict zones when it violates legal standards or democratic expectations

A clear red line here does two things: it preserves legitimacy and it creates a stable basis for coalition coordination. Allies can’t coordinate with ambiguity.

Restricted: Dual-use capabilities that need auditable controls

Restricted means allowed, but only with safeguards: oversight, logging, scope limits, and defined authorities.

This bucket is where most real-world national security AI lives. These are capabilities that help deterrence and resilience, but can drift into coercion or privacy violations if left unchecked.

Examples that fit restricted use:

• Predictive modeling of adversary decision-making to support crisis planning
• AI-assisted sanctions enforcement and illicit finance network detection
• Automated detection of disinformation and coordinated influence activity

Controls that should be non-negotiable in the restricted tier:

1. Auditability by design: immutable logs, provenance for data and outputs, and post-hoc review capability.
2. Scoped authorization: clear mission boundaries (who/what/where), time limits, and renewal requirements.
3. Attribution pathways: mechanisms that support confident internal attribution and policy accountability.
4. Misuse testing: red-team exercises focused on mission creep and cross-domain repurposing.

If you can’t explain who approved it, what it was allowed to do, and what it actually did—then you don’t control it.

Permissive: Defensive AI that stabilizes competition

Permissive means encouraged, because it raises resilience and reduces incentives for escalation.

This is the “defensive fortifications” category for AI in the gray zone—capabilities that protect democratic institutions and critical infrastructure without manipulating people.

Examples:

• Early warning systems for cyber and physical threats to critical infrastructure
• Anomaly detection for supply chain and port security
• AI-enabled identification of foreign disinformation (focused on detection and exposure, not counter-manipulation)

A simple test helps here: If the capability were publicly acknowledged, would it still make strategic sense? If yes, it’s more likely to be stabilizing.

How to “program boundaries” into AI for intelligence, surveillance, and mission planning

Answer first: Ethical boundaries aren’t a slide deck—implement them as technical constraints, operational gating, and governance hooks that prevent cross-domain misuse.

Teams building AI for national security often inherit a false division of labor: engineers build, lawyers review, operators employ. In gray zone AI, that’s too slow and too leaky. Boundaries must be expressed in artifacts engineers and operators actually touch.

1) Mission labels that travel with the model

Every deployable model should carry enforced metadata:

• Approved sphere(s): diplomatic / information / military / economic
• Allowed task types (detect, classify, recommend, generate)
• Prohibited outputs (e.g., synthetic identities, persuasive targeting)
• Data handling constraints (retention, sharing, domestic filters)

This is how you prevent “we built it for defense” from becoming “we used it for persuasion.”

2) Output constraints and “no-go” capabilities

If a system is in the information sphere for detection, it shouldn’t have latent abilities to generate mass persuasion content at scale.

Concrete design choices:

• Remove or gate generation modules from detection pipelines
• Rate-limit or watermark generated content for internal use
• Block features that support persona creation, mass DM automation, or emotion-optimized targeting

This is boring engineering. It’s also where escalation risk is actually reduced.

3) Human accountability that matches the risk

“Human-in-the-loop” becomes meaningless if the loop is too fast or too opaque.

A better standard: human accountability mapped to consequence.

• Low consequence (permissive): routine review and monitoring
• Medium consequence (restricted): named approving authority, audit logs, periodic re-certification
• High consequence (near prohibited): senior-level authorization and independent oversight

If an action could plausibly trigger retaliation, it needs a human decision point with real friction.

4) Pre-commitment in policy: what you won’t do even if you can

The most stabilizing move in gray zone competition is credible restraint. Not moral theater—strategic clarity.

If the U.S. signals that it won’t run AI-driven deception at scale, it strengthens coalition alignment and reduces the “race to the bottom” dynamic. It also gives policymakers a clearer foundation for countermeasures and norm-building.

Common questions leaders ask (and the clearest answers)

“If adversaries use AI manipulation, don’t we need to do it too?”

You need counter-influence, not mirror-image manipulation. Detection, attribution support, exposure, and resilience-building scale better in coalitions than offensive deception does. Also: deception tools boomerang. They get copied, adapted, and used against domestic audiences.

“Can we draw lines without giving up advantage?”

Yes—because advantage in the gray zone is often about trust, access, and coordination, not just capability. Prohibiting certain AI uses can increase access and legitimacy, which creates durable advantage.

“What’s the fastest way to make this real inside a program office?”

Adopt the three-tier framework as a procurement requirement:

• Prohibited: exclude from requirements and testing; disallow subcontractor development
• Restricted: require audit logs, scope controls, and misuse testing
• Permissive: prioritize funding and fielding

If it isn’t in acquisition language, it won’t survive contact with delivery schedules.

The next 12 months will set habits that last

Gray zone AI is already a daily reality in intelligence analysis, cyber defense, surveillance, and strategic communications. The question isn’t whether AI will be used. It’s whether the U.S. will institutionalize boundaries that prevent accidental escalation and preserve legitimacy.

If you’re building or buying AI for national security missions, this is the practical standard I’d use: separate the spheres, categorize the capability, and enforce constraints technically—not just rhetorically.

If you want to pressure-test your own program, start with one hard question: Which AI capability in your stack could be repurposed tomorrow for manipulation or coercion—and what would stop it?