When the fabrication becomes the weapon.
Synthetic media generation now lets belligerents corrupt the records, communications, and identifications that make a captured person legible as a human being owed dignity. AIHL maps where generative AI threatens prisoner-of-war protections — so the harm becomes foreseeable, and therefore preventable.
From Hague law to Geneva law
The AI-in-conflict debate has matured around the conduct of war. It has been near-silent on how AI renders war more inhumane.
Reorient the discourse
Move from Hague-law concerns about the conduct of hostilities toward Geneva-law concerns: the protection of individual human dignity in captivity.
Adapt a threat model
AIHL adapts the MAESTRO agentic-AI framework to assess the harm surface that synthetic media create for POW entitlements under GCIII.
Lay the groundwork
Found future technical and policy measures — this platform, red-teaming exercises, and governance mechanisms — to address the vulnerability before harms occur.
Just as much as the laws of war must become a political priority, so too must they become a sociotechnical priority on the part of all AI actors. — On the case for a sociotechnical turn in red-teaming AI systems in armed conflict
A fragile information regime
Why POW protection depends on records, contact, and identity staying valid, accessible, and attributable.
The AIHL framework
Seven layers, six IHL adaptations, and six analytic steps for mapping where synthetic media threatens entitlements.
Walk a scenario
Step through three worked cases as a responsible actor: decompose, map threats, trace the chain, mitigate.
Who defends each layer
A distributed-defense matrix that places the burden where capacity sits, and never on the victims.
The work behind it
The peer-reviewed basis, the sociotechnical-turn argument, and where the project goes next.
Bring it to your work
For AI labs, humanitarian institutions, and policymakers ready to pressure-test these protections.
The POW information regime
POW protections do not depend on a single record. They depend on a fragile, distributed information regime that must satisfy three conditions to function at all.
Validity
Information about POWs — records, communications, identifications — corresponds to reality.
Access
Information can be readily reached by the right stakeholders: families, home and detaining states, the ICRC, and the prisoner themselves.
Attributability
Information carries clear provenance, allowing it to be traced back to a legitimate actor.
The AIHL framework
MAESTRO's seven-layer architecture maps cleanly onto the pipeline that produces POW-targeted synthetic media — data → model → orchestration → deployment → ecosystem. Six targeted adaptations re-anchor it on humanitarian outcomes and IHL.
Scenario explorer
Select a scenario, then step through the framework as a responsible actor would — decomposing the system, mapping threats across layers, tracing the attack chain, and assigning mitigations.
Who is the responsible actor?
Decomposing the regime into layers reveals who is best positioned to defend each one. Responsibility is shared and continuous — and it never falls on the victims of harm.
The work behind the project
The AIHL Project rests on a straightforward argument: the protection of people hors de combat deserves the same sociotechnical scrutiny the field already gives to the conduct of hostilities.
A sociotechnical turn for the laws of war
Debate over artificial intelligence in armed conflict has matured around Hague law — targeting, proportionality, weapons review, escalation. It has been near-silent on how the same technologies make war more inhumane for those already in custody.
This work reorients that debate toward Geneva law: the dignity, contact, and accurate record-keeping owed to prisoners of war. It shows how generative synthetic media can corrupt the fragile information regime on which POW protection depends, and adapts the MAESTRO agentic-AI threat-modeling framework into AIHL, an instrument that maps the harm surface across seven layers and six analytic steps.
Three worked scenarios — a propaganda deepfake, falsified capture cards, and an audio-cloned call to a prisoner's family — demonstrate the model, and a distributed-responsibility analysis assigns defensive obligations in proportion to capacity, never to the victims. AIHL's central demand is that AI actors treat the laws of war as a sociotechnical priority, not only a political one.
6 analytic steps · 3 worked scenarios
Grounded in GCIII & customary IHL
Scenario library
An openly navigable, growing library of worked cases, expanding as new synthetic-media threats are analysed.
Red-teaming exercises
Operationalizing AIHL as adversarial evaluation against frontier models, with structured probes and scoring.
Governance mechanisms
Folding synthetic-media harms into IHL governance, accountability, and cross-actor reporting before harms occur.
Pressure-test the protections, before they fail
AIHL is built to be used. It is most valuable in the hands of the actors best positioned to find and close these gaps in their own systems and mandates.
The harm surface here is foreseeable, which means it is preventable. Whether you build the models, safeguard the platforms, hold the humanitarian mandate, or write the rules, AIHL gives you a shared language for naming where synthetic media threatens prisoner-of-war protections — and a method for deciding what to do about it.
Harden generative pipelines against the jailbreaks and misuse pathways the scenarios trace.
Detect and interdict synthetic POW media before it reaches families, states, and the record.
Anchor evaluation on humanitarian outcomes and escalate failures of the information regime.
Bring record-integrity and dignity harms explicitly into IHL governance and accountability.
Scope
A briefing maps AIHL onto your systems, mandate, or portfolio and identifies the layers most exposed.
Evaluate
Worked scenarios and red-teaming exercises pressure-test those layers against realistic adversaries.
Operationalize
Findings become concrete mitigations and monitoring, assigned in proportion to your capacity.
Bring AIHL to your organization.
Request a briefing, propose a red-teaming engagement, or talk through how the framework applies to your work.