Feb 2, 2026
Insurance-Grade Data Infrastructure for Frontier AI Governance
Subramanyam Sahoo
This project proposes an insurance-grade data infrastructure framework for frontier AI governance that addresses the critical challenge of including non-state actors—such as frontier labs, cloud providers, and model deployers—in international AI safety agreements through market-based mechanisms rather than state enforcement alone. The core contribution is formalizing the data problem that blocks effective AI insurance: frontier model incidents exhibit heavy-tailed loss distributions, correlated systemic shocks across organizations, and severe under-reporting, which conventional actuarial pricing cannot handle. The paper specifies a minimal set of standardized reporting fields covering exposure, controls, and incidents; proposes a governance architecture that conditions market access and compute supply chains on qualified insurance coverage tied to risk data submission; and demonstrates through stress-testing simulations that standardized confidential reporting coupled with baseline controls significantly reduces insurance exclusions and creates meaningful incentive gradients for safety practices, while systemic risk facilities are necessary to address correlated tail risk that private markets alone cannot sustain.
Interesting issue for AI liability insurance. I haven't heard of this data availability (to insurers) and incentive (for developers) problem being explicitly mentioned anywhere before. I also haven't seen the combination of insurance and international agreements before, despite working on both of those areas of AI governance.
"We propose [an insurance requirement] mechanism that can be written into an international agreement and implemented domestically." I'm very sympathetic to this idea and think it's a good suggestion.
Insurance is a fascinating research area-- the author correctly points out that there are some unique challenges associated with applying insurance to AI. Most notably, insurance is difficult to conceptualize in areas where the risks are so high in magnitude (potentially existential) and difficult to assess RE likelihood (lots of debate; unclear how to translate evals into probabilistic risk estimates).
The paper seems to focus on analyzing information that insurers/auditors would want access to, rather than addressing some of these central problems RE whether or not the insurance model "works" in this context. In future work, the author could try to grapple with these central questions and assess whether the assumptions behind the "insurance model" are fatally challenged in the context of frontier AI risks.
Cite this work
@misc {
title={
(HckPrj) Insurance-Grade Data Infrastructure for Frontier AI Governance
},
author={
Subramanyam Sahoo
},
date={
2/2/26
},
organization={Apart Research},
note={Research submission to the research sprint hosted by Apart.},
howpublished={https://apartresearch.com}
}
