The Insurability Frontier of AI Risk: Mapping Threats to Affirmative Coverage, Silent Exposures, and Exclusions
Alex Leung, Rex Zhang, Ervin Ling, Kentaroh Toyoda, SiewMei Loh
Why It Matters
What makes this one worth your time
Understanding the insurability of AI risks is crucial for AI engineers and researchers to navigate potential liabilities and ensure adequate coverage in an increasingly AI-driven world.
The paper maps the complex landscape of AI risk insurability across various insurance products.
Summary
The paper explores the emerging challenges in insuring AI-related risks by analyzing 55 AI threat classes against 26 insurance products and exclusion regimes, identifying a four-tier insurability frontier and highlighting patterns in AI coverage differentiation.
Key contributions
- Mapping AI threat classes against insurance products to identify coverage patterns.
- Identifying a four-tier insurability frontier for AI risks.
Notable insights
- The differentiation of AI coverage is beginning to align with specific risk emphases, such as model performance, hallucination, and autonomous system liability.
- Foundation model concentration presents a novel insurability frontier due to the potential for correlated losses across multiple clients.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2605.18784v1 Announce Type: cross Abstract: The rapid diffusion of agentic AI has created a new coverage problem for commercial insurance: some AI-mediated losses are now affirmatively insured, some create silent-AI exposure under legacy cyber, technology errors-and-omissions (E&O), directors-and-officers (D&O), employment practices liability (EPLI), crime, and media policies, and others are being actively excluded. This paper maps that emerging boundary by coding 55 AI threat classes against 26 insurance products, endorsements, and exclusion regimes using public carrier materials and OWASP/MITRE threat catalogs. We identify a four-tier insurability frontier: affirmatively insured perils, silent-AI exposures, actively excluded perils, and perils outside conventional private insurance structures. Our coding measures publicly claimed positioning rather than executed contract wording; the headline statistics describe what carriers publicly state about coverage, not what would be paid in any specific claim. Three patterns emerge. First, affirmative AI coverage is beginning to differentiate by primary risk emphasis: public materials often position Munich Re around model performance and drift, Armilla and parts of the Lloyd's market around hallucination and broader AI liability, Tokio Marine Kiln and CFC around IP and technology E&O concerns, Apollo ibott around emerging autonomous system liability, and Coalition around deepfake and AI-enabled cyber response. Second, legacy lines retain silent-AI exposure where AI is an instrumentality rather than the legal cause of loss. Third, foundation model concentration is the clearest genuinely novel insurability frontier because upstream model failure can correlate losses across many cedents at once; the relevant market design question is which insurability constraint each candidate structure relaxes, not merely which systemic risk template exists.