Is it accurate?
The record should reflect the person’s identity, location, and condition.
The video, the voice,
the record may not be.
We study how AI-generated media can undermine the protection of people in war. Our first focus is prisoners of war: their dignity, contact with family, and the records that identify them.
Explore the casesProtection depends on information people can trust.
A capture card tells a family where someone is held. A message provides contact with home. A record can help establish what happened in captivity.
Synthetic media can falsify each of these. It can also make genuine evidence easier to dismiss. We examine the systems that produce, handle, and distribute this information—and where safeguards are needed.
The record should reflect the person’s identity, location, and condition.
The right people need access: prisoners, families, authorities, and humanitarian intermediaries.
The source and history of a record need to be traceable.
Explore the information used at each stage.
A fabricated record passes through a system. So should the analysis.
MAESTRO4IHL adapts Ken Huang’s MAESTRO framework to examine synthetic-media risks to prisoners of war. Seven layers connect model behavior, records, distribution, oversight, and the people affected.
Use the frameworkOur adaptation and analysis are independent. Framework attribution and sources
Three ways synthetic media could harm a prisoner of war. Choose a case, then examine the system, the risks, and the proposed response.
These examples assume that Geneva Convention III applies. Risk ratings are qualitative research judgments, not measured probabilities or legal findings.
Different actors can protect different parts of the system. This matrix sets out proposed contributions based on their capabilities. It does not replace or redistribute legal obligations.
The burden does not fall on the prisoner. People in captivity and their families should not have to solve the technical problems that put them at risk.
We bring humanitarian questions into AI evaluation. The aim is to identify how a system could cause harm, test its safeguards, and document what still needs to change.
A threat model framework for synthetic media harms to POW protections under IHL.
Our first study examines propaganda video, falsified capture cards, and voice impersonation. It connects technical failure to effects on dignity, contact, and the reliability of records.
AI for Peace · NeurIPS 2026Paris · 12 December 2026
Extend the casebook as new capabilities and risks emerge.
Develop tests that examine model behavior and the systems around it.
Translate findings into safeguards, review processes, and reporting.
The Convention addresses prisoners of war in international armed conflict. The applicable rules depend on the facts and legal classification of a conflict.
MAESTRO was created by Ken Huang and published through the Cloud Security Alliance. MAESTRO4IHL applies and extends its layered threat-modeling approach. Our scenarios, mappings, and recommendations are our own.
Read Ken Huang’s original MAESTRO framework ↗
The AIHL Project is not affiliated with, sponsored by, certified by, or endorsed by Cloud Security Alliance, MAESTRO, Ken Huang, Distributed Apps, the ICRC, or any state or humanitarian institution unless expressly stated in writing.
This research is not legal advice or a formal determination of compliance with international humanitarian law.
Nathan Heath and Wm. Matthew Kennedy founded the AIHL Project to study how synthetic media can affect people protected by the laws of war. MAESTRO4IHL is our first focused study.

AI security & national security
Nathan is a decision scientist and AI safety researcher with 14 years of experience across geopolitics and emerging technology. He founded Syntony and has conducted adversarial evaluation through OpenAI and Anthropic’s private safety programs.
He holds an M.A. in Law and Diplomacy from The Fletcher School.
Nathan Heath is a decision scientist and AI safety researcher with 14 years of experience in geopolitics and emerging technology. He founded Syntony, where he leads AI evaluation, governance, risk advisory, and software work. Previously, as a Senior Research Scientist at National Security Innovations, he worked on emerging technology risk, AI integration, and security cooperation for U.S. government clients.
He red-teams frontier AI systems for OpenAI and Anthropic and serves as an Expert Advisor to the Cloud Security Alliance. He is a Security Fellow with the Truman National Security Project, a member of MIT AI Alignment, and a contributing researcher with the Oxford Martin AI Governance Initiative. His research examines system dynamics and frontier AI risk, the protection of testimony archives from synthetic media, and defense industrial cooperation in Europe.
Nathan holds an M.A. in Law and Diplomacy from The Fletcher School at Tufts University. He also studied Politics, Philosophy, and Economics at Oxford, and diplomatic and business strategy at Harvard. His work has been presented at UNIDIR, the International Association for Safe and Ethical AI at UNESCO House, the Cambridge Centre for Geopolitics, the Minderoo Centre for Technology & Democracy, the American University of Paris, the International Institute for Justice, and the UK Ministry of Defence’s Deterrence and Assurance Academic Alliance. It has appeared in War on the Rocks, RAND Europe, World Politics Review, PRISM, and The Washington Post.
He leads the North Carolina chapter of the OpenAI Forum and contributes research to the Meta Oversight Board and the EvalEval Coalition.

AI ethics & international law
Matt is an AI+ Senior Research Fellow at King’s College London. He researches AI ethics, evaluation, and political economy. His work brings the history of colonialism and international law into questions of AI safety and governance.
He received his PhD from the University of Sydney.
Wm. Matthew (Matt) Kennedy is an AI+ Senior Research Fellow at King’s College London and a former Marie Skłodowska-Curie Postdoctoral Fellow at the Oxford Internet Institute. His research examines AI ethics, safety, and political economy, with particular interests in evaluation and red-teaming, AI and knowledge production, education, epistemic harms, and decolonial AI.
He is affiliated with the University of Sydney Centre for AI, Trust, and Governance; the Machine Learning & Society team at Women at the Table; the AI and Education at Oxford University research hub; and the Center for AI and Digital Policy. He regularly contributes to Humane Intelligence events and is an inaugural ORF US-India AI Fellow.
Matt earned his PhD at the University of Sydney, studying the history of colonialism, international law, and scientific governance. He subsequently held research fellowships at the University of Texas at Austin and the University of Sussex. In the technology industry, he led a small AI evaluation team, collaborated on research in AI ethics, safety, and governance, and advised U.S. and international public officials on AI policy.
At the Oxford Internet Institute, his project examined how the colonial past can inform decisions about the design, development, use, and governance of AI systems.
We welcome researchers, AI developers, humanitarian practitioners, and public institutions working on these questions.
Write to discuss a research collaboration, a briefing, or an evaluation.
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