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Sprint projectFeb 2, 2026India

Insurance-Grade Data Infrastructure for Frontier AI Governance

Subramanyam Sahoo

Submitted to The Technical AI Governance Challenge. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Insurance-Grade Data Infrastructure for Frontier AI Governance

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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.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. 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.

  2. 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 project

@misc{sahoo2026insurancegrade,
  title = {{Insurance-Grade Data Infrastructure for Frontier AI Governance}},
  author = {Subramanyam Sahoo},
  year = {2026},
  month = feb,
  note = {Submitted to The Technical AI Governance Challenge, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/insurancegrade-data-infrastructure-for-frontier-ai-governance-x52g}},
  url = {https://apartresearch.com/sprints/projects/insurancegrade-data-infrastructure-for-frontier-ai-governance-x52g}
}

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