AI for Peace Deals: Modeling a Ukraine-Russia Settlement

A “peace plan” that tries to pre-decide the hardest questions usually fails for a simple reason: war doesn’t negotiate like a spreadsheet. Lines on a map are tied to artillery ranges, drone corridors, logistics capacity, domestic politics, and verification realities that don’t show up in bullet points.

That’s why Ryan Evans’ 15 principles for a Ukrainian–Russian settlement (written as a response to a noisier multi-point proposal circulating in late 2025) are useful even if you disagree with parts of them. They shift the focus from “announce a deal” to design a negotiation system—one that separates ceasefire mechanics from sovereignty, treats verification as central, and acknowledges coercion and incentives as part of the process.

For this AI in Defense & National Security series, this is also a practical case study in where AI actually helps. Not “AI writes the treaty.” More like: AI helps negotiators and planners test assumptions, model security architectures, forecast compliance risk, and stress‑test implementation details—before those details become casualties or headlines.

The real problem: peace plans fail at the interface between politics and physics

Answer first: Peace proposals break when they ignore operational constraints—especially verification, force posture limits, and the messy gap between a ceasefire line and a recognized border.

The RSS article highlights a familiar failure mode: a plan that attempts to “solve” territorial outcomes up front, mixes outdated arms control references, and draws restrictions in ways that don’t match how modern strikes work (for example, limiting certain long‑range missiles while saying little about drones or other systems). That’s not just a drafting issue. It’s a strategic credibility issue.

Here’s the thing about modern conflict: the weapon mix changes faster than diplomacy. By winter 2025, low-cost drones, electronic warfare, dispersed logistics, and rapid targeting cycles mean that “buffer zones” and “demilitarization” can’t be treated as symbolic. They’re technical systems problems.

What AI adds at this stage

AI doesn’t replace statecraft, but it can prevent unforced errors by giving negotiators an empirical backstop:

• Range-and-effects modeling to test whether proposed demilitarized zones actually reduce risk—or simply push fires to different systems.
• Logistics feasibility checks for withdrawal timelines, redeployments, and sustainment under monitoring.
• Ceasefire violation forecasting based on terrain, unit proximity, historical incident patterns, and sensor coverage.

A useful one-liner for any negotiation team: If you can’t verify it, you can’t stabilize it.

Why Evans’ “principles first” approach is structurally stronger

Answer first: Negotiations are more likely to start (and survive) when parties agree on process and constraints before arguing over the final political endpoints.

Evans’ framework is explicitly not a finished peace treaty. It’s a set of conditions that can get both sides to say “yes” to negotiations without forcing immediate acceptance of final outcomes.

Three design choices matter.

1) Separate ceasefire lines from sovereignty claims

Evans argues for a core principle: a ceasefire line can be temporary and decoupled from legal recognition. That sounds abstract, but it’s the difference between:

• “Stop shooting where you are, then negotiate status later,” and
• “Concede status now in order to stop shooting.”

The first is negotiable. The second is often political suicide.

Where AI helps: You can model multiple “freeze” geometries and quantify risk tradeoffs—how line curvature, river crossings, urban edges, and road networks affect:

• likelihood of tactical incidents
• time-to-escalation from a localized clash
• monitoring burden (number of observers, drones, radar posts)

This is exactly the sort of decision that looks simple in a PDF and turns deadly in execution.

2) Put verification and enforcement architecture at the center

Evans emphasizes enforceable security guarantees and a robust monitoring mission (UN/OSCE-style or hybrid). That’s not bureaucracy. It’s the mechanism that keeps a ceasefire from becoming a reloading period.

Where AI helps: Monitoring at scale is a data problem. AI can fuse ISR feeds—satellite imagery, UAV video, acoustic sensors, ground reports—into:

• anomaly detection (new berms, ammo dumps, bridging equipment)
• pattern-of-life changes near restricted zones
• probabilistic confidence scoring for violations

This also supports diplomacy: when accusations fly, a verification body needs defensible, explainable evidence, not vibes.

3) Treat “strategic stability” as part of settlement, not a footnote

The framework includes nuclear safety and special protocols for nuclear energy infrastructure (explicitly including the Zaporizhzhia plant under IAEA oversight). That’s a reminder that strategic stability isn’t separate from conventional war when strikes, sabotage, and grid attacks are on the table.

Where AI helps: Risk models for critical infrastructure can connect threats to consequence:

• probabilistic scenario planning for grid failures
• cascading effects modeling (power → water → hospitals → displacement)
• prioritization of protection measures under resource constraints

Buffer zones and force limits: the part everyone hand-waves (and shouldn’t)

Answer first: Demilitarized zones and conventional force limits are only stabilizing if they’re measurable, monitorable, and resilient to drone warfare and deception.

Evans proposes demilitarized/buffer zones and mutual limits on troops and heavy weapons. Historically, this logic tracks with arms control and confidence-building measures. The modern twist is that “heavy weapons” no longer define lethality.

A ceasefire can fail even when tanks are parked—because:

• FPV drone teams act like precision artillery
• EW systems can blind monitoring drones
• long-range fires can be improvised from modular platforms

A practical way to modernize “force limits” for 2026 realities

If you’re building technical annexes today, consider adding categories beyond classic equipment lists:

1. UAS density limits by class (micro, short-range, MALE) within defined belts
2. EW emissions constraints and reporting requirements near monitoring corridors
3. Launch signature monitoring commitments (radar/acoustic arrays) to detect unreported fires
4. Logistics throughput caps (fuel, ammunition tonnage) into restricted areas as a proxy for offensive capacity

Where AI helps: These measures require continuous inference, not occasional inspections. AI systems can estimate force posture using indirect indicators—traffic flow, thermal patterns, supply chain activity—while flagging what needs human verification.

A demilitarized zone is not a line; it’s a surveillance-and-compliance system.

Humanitarian provisions and justice: where analytics can prevent false choices

Answer first: Humanitarian outcomes improve when negotiations treat detainees, children, and civilian returns as operational workflows—tracked, audited, and deconflicted across agencies.

The article calls for:

• POW exchange “all for all”
• return of civilian detainees
• a working group for family reunification and children’s return

It also proposes a differentiated justice mechanism—amnesty for many combatants, while separating command responsibility and carve-outs for serious crimes.

This is morally charged territory, and it should be. But there’s also a hard operational point: humanitarian measures fail when identity, custody, and movement data are fragmented.

What AI can do without turning humans into “cases”

A responsible approach is to use AI for integrity and throughput, not for moral judgment:

• Entity resolution across inconsistent records (names, transliterations, missing documents)
• Fraud and coercion detection in repatriation pipelines
• Queue optimization for medical evacuation and reunification logistics
• Audit trails that reduce opportunities for hostage-taking-by-bureaucracy

The non-negotiable requirement: human oversight, explicit consent where possible, and strong data minimization. In national security contexts, “we’ll secure it later” is how scandals start.

Economic normalization, sanctions, and the missing analytic layer

Answer first: Sanctions relief and reconstruction funding work when they’re staged against verifiable milestones—and when planners can measure compliance faster than violations can compound.

Evans’ framework ties sanctions relief to verified compliance and argues for investment-oriented reconstruction mechanisms. It also includes freedom of navigation in the Black Sea and protection of critical infrastructure.

This is where negotiation teams often lack a shared dashboard. One side claims compliance; the other disputes it; markets and publics react before facts settle.

Where AI-driven decision support is strongest

• Milestone tracking that binds legal text to observable indicators (withdrawal confirmed, heavy weapons relocated, shipping corridors open)
• Evasion network analytics to identify recurring sanctions-bypass patterns (shipping behavior, insurance anomalies, financial routing)
• Reconstruction prioritization using damage assessments, population return forecasts, and supply constraints

AI here isn’t about punishing. It’s about aligning economic incentives with measurable behavior.

Implementation is the deal: building a “negotiation digital twin”

Answer first: The highest-leverage AI application for peace talks is a digital twin that simulates ceasefire, verification, logistics, and escalation pathways under uncertainty.

If you take one idea from this post, make it this: negotiation teams need a shared technical capability that can answer “what happens if…?” in hours, not weeks.

A negotiation digital twin combines:

• geospatial terrain and infrastructure data
• order-of-battle estimates and force posture assumptions
• sensor coverage and monitoring constraints
• logistics capacity models (roads, rail, depots, seasonal weather impacts)
• escalation dynamics (incident-to-response chains)

Then it runs scenario bundles:

• “What DMZ depth reduces incident probability by 30% without making monitoring impossible?”
• “What withdrawal timeline is feasible without creating a security vacuum?”
• “How many observers and drones are required to achieve 95% detection confidence for heavy equipment moves?”

This is where defense AI and national security AI become concrete: planning, forecasting, and verification support—not autopilot diplomacy.

What leaders should ask before they endorse any multi-point plan

Answer first: A credible plan answers verification, incentives, and escalation control—not just end-state slogans.

If you’re a policymaker, staffer, or security leader evaluating a proposed settlement framework, use this checklist:

1. Verification: Who monitors? With what authorities? What happens after a violation?
2. Modern weapons reality: Does it address drones, EW, and strike systems, or only legacy categories?
3. Ceasefire vs sovereignty: Are these separated to allow a stop to fighting without pre-baked capitulation?
4. Implementation tempo: Are timelines operationally feasible in winter conditions and under contested information?
5. Dispute resolution: Is there a joint commission that can prevent minor incidents from cascading?
6. Economic sequencing: Are sanctions relief and reconstruction tied to measurable milestones?

If a plan can’t answer these, it’s not a settlement framework. It’s a press release.

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

This series keeps coming back to a theme: AI is most valuable where national security work is high-volume, high-uncertainty, and high-stakes—ISR fusion, cyber defense, mission planning, logistics, and decision support.

A Ukraine–Russia settlement framework is a concentrated example. Buffer zones, arms limits, monitoring, nuclear facility protocols, sanctions sequencing—each is a technical system with political consequences. AI won’t create trust, but it can reduce blind spots and shorten the time between signal and decision.

If your organization is exploring defense AI, start with the parts that are easiest to justify and govern: verification analytics, logistics forecasting, and scenario simulation tied to clear human decision rights.

The next serious peace process—wherever it happens—will be negotiated in conference rooms and enforced in data streams. Are we building the tools for that reality, or pretending it doesn’t exist?