AI Deterrence: Restore U.S. Credibility vs. Putin

Deterrence doesn’t fail in one dramatic moment. It fails in a sequence of “small” moments that adversaries interpret as permission.

Russia’s pattern is familiar: Georgia in 2008, Crimea in 2014, and then the full-scale invasion of Ukraine in 2022. Ambassador Joseph DeTrani’s argument is blunt and, frankly, hard to dispute: U.S. and NATO deterrence credibility eroded over time, and Putin acted on that assessment.

Here’s the part many leaders still underestimate: restoring deterrence in 2025 isn’t only about how many systems you can ship, or how many sanctions you can announce. It’s about whether the United States and its allies can see, attribute, decide, and respond faster than an adversary can exploit ambiguity. That’s where AI in defense and national security stops being a tech trend and becomes a strategic requirement.

Deterrence credibility is a perception problem—fed by data

Deterrence credibility is simple to describe and hard to maintain: your adversary must believe aggression will cost more than it’s worth, and that those costs will be imposed reliably.

DeTrani points to a long arc of signaling problems—limited punitive measures after Georgia, muted consequences after Crimea, and an inability to prevent the 2022 invasion despite credible intelligence. Those episodes matter because adversaries don’t grade you on intentions; they grade you on outcomes.

Why “red lines” keep failing in hybrid warfare

Hybrid warfare is designed to sit in the gray zone:

• sabotage and covert action that’s deniable
• cyber operations that blur state and criminal actors
• information operations that degrade cohesion and political will
• incremental territorial gains that avoid a single “tripwire moment”

If deterrence relies on clarity, hybrid warfare relies on confusion. Ambiguity is the weapon. And when ambiguity persists long enough, it becomes a strategic narrative: the West won’t respond decisively.

AI helps most at the exact point hybrid warfare tries to break you: making ambiguity expensive through faster detection, attribution, and coordinated response.

The Ukraine war shows the new deterrence equation: time-to-decision

A major lesson from Ukraine isn’t just about fires and maneuver; it’s about tempo.

Modern deterrence depends on time-to-decision—how quickly a national security system can translate signals into an actionable, lawful response. If an adversary can act faster than you can coordinate, they can repeatedly create faits accomplis.

DeTrani highlights that the U.S. had credible intelligence before the February 2022 invasion but failed to convince Putin the costs would be immediate and severe enough to change his calculus. That gap—between knowing and compelling behavior change—is where deterrence now lives.

AI’s role: compress the observe–orient–decide loop

In practical terms, AI in national security contributes in three ways:

1. Better sensing: fusing satellite imagery, SIGINT, OSINT, UAV feeds, logistics signals, maritime AIS anomalies, and cyber telemetry.
2. Faster interpretation: spotting patterns humans miss (or can’t process in time) and elevating “what matters now.”
3. More reliable options: generating response menus tied to policy constraints (rules of engagement, escalation boundaries, alliance commitments).

This isn’t science fiction. It’s the difference between:

• “We think something is happening”
• and “We can prove what’s happening, who’s doing it, and what we can do next—today.”

Deterrence credibility grows when adversaries expect rapid, defensible, coordinated consequences.

Where AI strengthens deterrence without escalating recklessly

AI doesn’t automatically make deterrence stronger. Used poorly, it can create false confidence, brittle automation, and escalation risk. Used well, it does something valuable: it makes responses more precise.

Precision matters because blunt responses are politically harder to sustain and easier to fracture across alliances. Precision responses—targeted sanctions, rapid interdictions, exposure of covert action, cyber defense and countermeasures—are more credible because they’re more usable.

1) Intelligence fusion that survives denial and deception

Russia’s security services and military planners expect Western debate, evidentiary standards, and alliance friction. They exploit delays.

AI-driven fusion can help by:

• correlating weak signals across domains (cyber + logistics + comms + imagery)
• detecting deception patterns (decoys, spoofed transmissions, manipulated narratives)
• producing audit-friendly analytic trails so policymakers can act confidently

A deterrence message lands differently when it’s backed by shareable, coalition-grade evidence, not just classified assertions that partners can’t validate.

2) Autonomous and semi-autonomous systems as “credible presence”

Deterrence isn’t only about punishment. It’s also about denial: making aggression unlikely to succeed.

Autonomous systems contribute to denial by enabling persistent presence at lower risk and cost:

• maritime drones that monitor chokepoints and suspicious activity
• ISR swarms that increase coverage and complicate adversary planning
• counter-UAS systems that adapt to new drone tactics in days, not months

I’ve found that the most persuasive deterrent posture is one that looks boring but unavoidable: persistent surveillance, fast classification, and a ready response that doesn’t require weeks of political choreography.

3) Cyber deterrence that’s measurable and repeatable

Cyber deterrence often fails because leaders can’t answer two questions fast enough:

• Are we under attack or just experiencing noise?
• Who did it, and can we say so publicly?

AI in cybersecurity improves deterrence by reducing false positives, speeding incident triage, and enabling attribution with higher confidence. The strategic effect is simple: fewer “free hits” for adversaries.

When adversaries believe cyber operations will be quickly detected and exposed—especially if exposure triggers real consequences—they become more selective. That selectivity is deterrence at work.

The credibility trap: deterrence can’t be episodic

One reason deterrence credibility erodes is that responses look improvised. Adversaries learn that the West’s action is conditional on the news cycle.

DeTrani argues that a credible deterrence strategy would have imposed stronger consequences earlier—biting sanctions, pariah status, and a likely military response. Whether or not you agree with every element, the strategic requirement is right: consequences must be predictable enough to be believed.

A practical model: “pre-committed consequence ladders”

A more credible approach in 2025 is to publish and rehearse consequence ladders—especially with allies—so responses don’t depend on improvisation.

For example:

1. Gray-zone sabotage confirmed → coordinated expulsions, financial actions, transport/logistics restrictions, public attribution
2. Cyber attack on critical infrastructure → joint cyber defense surge, sanctions on enabling entities, disruption operations
3. Cross-border escalation → rapid military reinforcement, expanded weapons support, maritime/air domain measures

AI supports this by providing trigger-quality indicators—signals robust enough that leaders can act quickly without arguing about basic facts.

“What about China, North Korea, and Iran?” Deterrence is contagious

DeTrani makes a crucial point: Russia’s partners and fellow travelers—China, North Korea, and Iran—watch how the Ukraine war ends and how enforcement works. Deterrence credibility is contagious in both directions.

• If Russia gains through aggression, it normalizes territorial revisionism.
• If Russia pays a sustained price and can’t translate violence into political reward, it discourages copycats.

This matters because U.S. deterrence is a portfolio, not a single account. Taiwan policy, extended deterrence commitments on the Korean Peninsula, and Middle East security commitments all influence one another.

AI can’t replace diplomacy or force posture. But it can improve the one thing that ties these theaters together: decision advantage—the ability to see the situation clearly and act faster than an adversary can exploit hesitation.

What leaders can do now: an AI deterrence checklist

If you’re responsible for defense planning, national security tech, or allied interoperability, here’s a pragmatic checklist that separates “AI pilots” from real deterrence capability.

Build AI systems that allies can actually use

Deterrence against Russia is almost always coalition deterrence. That means:

• shared data standards and releasable analytic outputs
• multilingual information operations monitoring
• joint exercises that include AI workflows (not just platforms)

A model that can’t produce coalition-consumable evidence won’t move deterrence credibility.

Measure time-to-decision as a strategic metric

Track, improve, and rehearse:

• time from detection to attribution
• time from attribution to decision options
• time from decision to coordinated action

If these numbers aren’t improving quarter over quarter, your deterrence posture is drifting.

Prioritize resilience over “perfect prediction”

Most companies and agencies get this wrong: they chase predictive perfection. Deterrence needs resilience.

Resilience means:

• systems that degrade gracefully under attack
• redundant sensing and comms
• models robust to deception and data poisoning
• human-in-the-loop controls for escalation-sensitive decisions

AI that collapses in the presence of adversary deception is worse than no AI—it invites miscalculation.

Where this fits in the “AI in Defense & National Security” series

This series is about practical advantage: surveillance, intelligence analysis, autonomous systems, cybersecurity, and mission planning that actually change outcomes.

Restoring deterrence credibility against Putin is a clean case study because it shows the real constraint: speed and coherence. The U.S. and its allies don’t just need capabilities. They need the ability to employ them decisively, repeatedly, and in a way adversaries believe.

If deterrence is the promise of future action, AI makes that promise believable by shrinking the gap between what we know and what we do.

The open question for 2026 isn’t whether AI will be used in national security—it already is. The question is whether democratic alliances can operationalize it fast enough, safely enough, and jointly enough to restore the kind of credibility that prevents the next war rather than merely managing the current one.