AI Red-Teaming Peace Deals: Lessons From Ukraine

A leaked 28-point proposal to end the war in Ukraine triggered a familiar strategic smell test: does the deal reduce violence now while increasing the odds of a larger war later? If the answer is “yes,” you don’t have a peace plan—you have a pause button.

Rob Dannenberg’s critique of the draft plan is blunt: it reads like concessions stacked in the aggressor’s favor, at a moment when Russia’s battlefield position looks constrained and its economy shows signs of strain (including reported 0.6% GDP growth in Q3 2025, layoffs at a major bank, and taboo-breaking moves like selling reserves). Whether you agree with every point or not, the underlying warning matters for anyone in defense and national security: bad deals are rarely “accidents.” They happen when decision-makers don’t have a disciplined way to test assumptions, quantify second-order effects, and spot manipulation.

This post reframes the peace-plan debate as a case study for the AI in Defense & National Security series: how modern AI—used correctly—can help leaders avoid “snatching defeat from the jaws of victory” by stress-testing negotiations, forecasting escalation risk, and exposing coercive terms before they calcify into policy.

The problem with “peace plans” that reward aggression

A peace deal is strategically dangerous when it trades near-term quiet for long-term vulnerability. In Ukraine’s case, the most criticized elements aren’t just territorial or military—they’re societal: language status, religious institutions, and enforcement mechanisms that could enable systematic repression.

Here’s the key national security point: coercive peace terms don’t merely end a phase of war; they can reset the battlespace for the next phase. They buy time for the aggressor to reconstitute forces, rewire information control, and normalize faits accomplis.

From an analytic perspective, these deals typically share three characteristics:

1. Asymmetric concessions: one party gives up durable assets (sovereignty, institutions, security guarantees) for reversible promises (ceasefires, future talks).
2. Weak verification: monitoring is vague, enforcement is political, and violations are deniable.
3. Narrative capture: the deal is framed as “pragmatic” or “inevitable,” discouraging scrutiny of downstream consequences.

That pattern is exactly where AI can help—not by making the decision, but by making it harder to sleepwalk into one.

What AI can add that traditional analysis often can’t

AI’s real advantage in national security decision-making is speed + breadth + consistency. It can ingest more signals than a human team, keep the logic trail intact, and run the same test repeatedly as conditions change.

Used responsibly, AI supports three things leaders routinely need during negotiations:

• Structured comparison of scenarios (best case, base case, worst case)
• Early warning when an adversary is preparing to violate terms
• Detection of manipulation in information operations around the deal

The reality? Most organizations still do these tasks with a mix of spreadsheets, expert judgment, and rushed briefs. That’s not incompetent—it’s just mismatched to the pace of modern conflict.

AI is good at “deal forensics”

A negotiation document is data. So are prior ceasefire texts, historical violations, propaganda patterns, sanctions responses, and force-generation cycles.

A well-built AI workflow can:

• Extract commitments and obligations into a machine-readable term sheet
• Tag clauses by risk type: security, governance, cultural repression, economic coercion, legal impunity
• Compare language against prior agreements associated with high violation rates
• Highlight clauses with high ambiguity (“as soon as feasible,” “appropriate measures,” “mutual consent”) that tend to be loopholes

This doesn’t replace diplomats or regional experts. It gives them a faster x-ray.

AI is good at “second-order effects” mapping

Humans are decent at first-order consequences (“this ceasefire reduces shelling”). We’re worse at second-order consequences (“this clause enables arrests, which fuels insurgency, which raises NATO escalation risk”).

Modern models can help generate and test causal graphs of plausible pathways, then tie them to measurable indicators. That matters when a plan includes societal control levers—language, religion, media—because the downstream impacts show up in:

• refugee flows
• internal resistance
• detention patterns
• economic resilience
• cross-border sabotage and covert action

If the deal increases the probability of those indicators moving in predictable directions, that’s not a footnote. It’s the story.

A practical framework: AI red-teaming a peace plan

Red-teaming is the discipline of trying to break your own plan before the adversary does. AI can make red-teaming faster, more repeatable, and less vulnerable to groupthink.

Below is a workflow I’ve seen work in real security organizations (with the usual caveat: governance and classified handling come first).

1) Translate the plan into testable claims

Start by converting the document into explicit claims, like:

• “A ceasefire will hold for 12 months.”
• “Monitoring will deter violations.”
• “Political concessions won’t trigger repression.”
• “Sanctions relief won’t accelerate rearmament.”

If a deal can’t be expressed as testable claims, it can’t be managed.

AI support:

• Clause extraction
• Commitment mapping (who promises what, by when, verified by whom)
• Ambiguity scoring

2) Build three futures, not one

Negotiations often assume a single “intended” future. That’s a mistake.

Model three futures:

1. Compliance future: both sides largely comply.
2. Gray-zone future: deniable violations, political intimidation, slow-rolling enforcement.
3. Spoiler future: rapid breakdown, escalation, false-flag incidents, or internal political sabotage.

AI support:

• Scenario generation constrained by historical patterns
• Simulation inputs from force posture, economic capacity, and seasonal factors (winter energy targeting is not theoretical—it’s operational)

3) Score the deal on “reversibility”

Here’s a simple, brutally effective test:

If one side cheats, can the other side recover what it gave up?

Territory, institutions, language policy, and security architecture are typically hard to reverse. Promises, ceasefires, and future referenda are typically easy to reverse.

AI support:

• Reversibility scoring matrix
• Precedent analysis across similar clauses in other conflicts

4) Stress-test verification and enforcement

Verification is not a box to check; it’s the whole deal.

Ask:

• Who collects data?
• What sensors and access exist?
• What’s the enforcement trigger?
• What happens after the first violation—politically and militarily?

AI support:

• Monitoring concept evaluation (what indicators would show cheating?)
• Collection plan suggestions across ISR, cyber, HUMINT, OSINT
• “Violation playbooks” tied to decision thresholds

5) Run an information operations audit

Dannenberg’s core warning is about manipulation—promises that function as bait.

A strong AI-enabled audit looks for:

• sudden narratives pushing “inevitability”
• coordinated amplification of “peace at any price” frames
• attempts to polarize domestic audiences to reduce support for enforcement

AI support:

• Narrative clustering
• Bot / coordinated behavior detection (paired with human validation)
• Cross-platform storyline tracking

Ukraine as the case study: what AI would flag early

If you apply the red-team workflow to a Ukraine-style draft plan, several risk clusters stand out fast. These are not ideological objections; they’re operational warnings.

Cultural control clauses are not “soft issues”

Clauses affecting language, religion, and governance aren’t symbolic. In occupied territories, they can become tools for:

• identity suppression
• loyalty screening
• justification for detention
• forced conscription

An AI term-extraction pass would likely classify these as high-impact, low-reversibility concessions—the worst combination.

Economic strain can be misread as strategic weakness

The article highlights signs of Russian economic pressure: modest growth, layoffs, reserve sales, sanctions bite.

That can support two opposing interpretations:

• Opportunity: pressure increases incentives to compromise.
• Danger: pressure increases incentives to seek a deal that restores cashflow and rearmament capacity.

AI helps by tying “economic relief” clauses to concrete reconstitution timelines: how fast procurement, munitions production, and force rotation can rebound under different sanctions regimes.

“Ceasefire first” can lock in battlefield advantages

When the front is relatively static, ceasefires can harden lines. If the aggressor holds territory, the deal may convert temporary occupation into durable control.

AI can map this into a clear risk statement:

A ceasefire that freezes current lines without credible enforcement increases the probability of renewed war within 24–48 months.

Even if you disagree on the exact number, the point is the same: freeze lines + weak enforcement = delayed conflict.

The leadership gap: AI won’t save a bad process

The biggest failure mode isn’t model accuracy. It’s decision discipline. If a leadership team wants “a deal” more than it wants “a stable outcome,” AI becomes window dressing.

Three governance practices separate real capability from slideware:

1. Decision logs: every major assumption is recorded with owner, confidence, and review date.
2. Model transparency: leaders see the variables that drive outputs, not just a score.
3. Adversary emulation: dedicated teams use AI to argue the adversary’s best case—because that’s what you’ll face.

If you’re running AI in mission planning, intelligence analysis, or negotiation support, this is the standard you should demand.

What defense and national security teams should do next

If your organization supports conflict analysis, diplomacy, or strategic planning, start with a “peace plan readiness checklist.” It’s the fastest way to turn AI ambition into operational value.

1. Build a clause-to-risk taxonomy tailored to your mission (territory, governance, cultural control, verification, sanctions, force posture).
2. Stand up a red-team pipeline that can ingest draft texts and produce: ambiguity score, reversibility score, enforcement gaps.
3. Integrate escalation modeling into the negotiation rhythm (daily or weekly updates, not quarterly studies).
4. Add an influence ops lens to every negotiation: who benefits from which narrative, and how it’s being pushed.

If you want leads from this post: the buyers aren’t “AI enthusiasts.” They’re leaders tired of finding out three months late that the deal was written to be violated.

The uncomfortable truth behind “Snatching Defeat from the Jaws of Victory” is that strategic failure often arrives wearing the costume of compromise. AI can’t guarantee wise choices—but it can make the costs, tradeoffs, and deception tactics much harder to ignore.

If you were tasked with advising on a high-stakes peace plan tomorrow, would your team be able to quantify what breaks first—and what happens next—within 48 hours? Or would you be forced to argue from intuition while the adversary argues from preparation?