The Insurability Frontier of AI Risk: Mapping Threats to Affirmative Coverage, Silent Exposures, and Exclusions

Battle Count: 60

This paper is entirely focused on AI risk insurability, insurance market structure, and coverage mapping. It has no direct relevance to quantitative trading, algorithmic execution, portfolio optimization, or market microstructure. The only tangential connection is the systemic risk/aggregation analysis (foundation model concentration) which shares conceptual parallels with correlated-loss modeling in finance, but the paper does not engage with trading strategies, asset pricing, or market data.

The methodology is a structured content analysis and classification exercise rather than a computational model. Implementation requires: (1) assembling a corpus of public carrier materials, regulatory filings, and trade press; (2) maintaining a 55-threat taxonomy aligned with OWASP/MITRE catalogs; (3) coding 1,430 cells with three independent human coders plus LLM-assisted retrieval; (4) applying four decision rules consistently; (5) adjudicating disagreements. The analytical framework (six insurability criteria, four-tier frontier) is conceptually straightforward but requires deep domain expertise in both AI security and insurance law. The main complexity is in maintaining currency as the market and threat landscape evolve rapidly.

The paper publishes the full 55×26 matrix in Appendix A, a replicable codebook with four decision rules, per-cell evidence pointers, and inter-rater reliability statistics in supplementary material. All URL-based sources were verified accessible as of May 6, 2026. The methodology is explicitly designed for replication and recoding as the market evolves. However, some carrier-specific exclusion filings (I-24) rely on trade press summaries where state-by-state forms are not public, and the OWASP Agentic Skills Top 10 remains an incubator project, introducing volatility.