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 how AI risks are insured is crucial for AI developers and businesses to manage potential liabilities and ensure adequate coverage.
The paper maps the insurability landscape of AI risks across different insurance products.
Summary
The paper explores the emerging challenges in insuring AI-related risks by categorizing AI threats against various insurance products and identifying a four-tier insurability frontier. It analyzes public materials from insurance carriers to understand how AI risks are currently positioned in terms of coverage.
Key contributions
- Mapping of AI threat classes against insurance products and exclusion regimes.
- Identification of a four-tier insurability frontier for AI risks.
Notable insights
- The differentiation of AI coverage is beginning to emerge based on primary risk emphasis, such as model performance and AI 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:2605.18784v2 Announce Type: replace-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.