Why This Paper Changes How Every Chief Risk Officer Should Think About AI

The commercial insurance market has been working on AI-specific products for roughly two years. Munich Re launched aiSure. Armilla and Lloyd’s syndicates developed hallucination warranties. Coalition added deepfake response endorsements. Apollo ibott created a standalone agentic liability product.

But here is what was missing: no one had drawn the full picture. Individual carriers positioned their products. Brokers advised their clients based on partial information. Policyholders had no way of knowing whether their existing cyber, tech E&O, D&O, employment practices liability, or crime policies would respond to an AI-caused loss.

This paper fills that gap at industrial scale. The authors constructed a 55-by-26 matrix — 55 AI threat classes drawn from OWASP and MITRE threat taxonomies, mapped against 26 commercial insurance products, endorsements, and exclusion regimes using public carrier filings, market intelligence, and trade press reporting.

The result is the first evidence-based map of where the AI insurance market actually stands. And it reveals a market in active bifurcation — splitting into specialized affirmative products while legacy lines accumulate silent exposure that almost no one has quantified.

For a Chief Risk Officer or Chief Financial Officer, this paper answers the single question that should keep you up at night: “If one of our AI agents causes a material loss, will our insurance respond?”

The answer, in most cases, is “we do not know — and here is how to find out.”

Methodology, Explained Simply

Imagine you own a house and want to know which disasters your homeowners insurance covers. You would read the policy. But what if the policy was written before houses had electricity, and your insurer has never updated the wording to say whether it covers electrical fires?

That is the state of AI insurance today.

The authors took two giant lists and cross-referenced them. The first list was 55 AI threat classes — everything from hallucination and model drift to algorithmic bias, IP infringement, deepfake impersonation, and the failure of a foundation model that hundreds of companies depend on. These threats came from established industry taxonomies (OWASP Top 10 for LLM Applications and MITRE ATLAS).

The second list was 26 commercial insurance products — the standard policies that large companies carry: cyber insurance, technology errors and omissions (tech E&O), directors and officers liability (D&O), employment practices liability (EPLI), crime insurance, media liability, general liability, and commercial property, plus newer AI-specific endorsements and exclusions.

For each of the 1,430 combinations, the authors coded the market position into one of four categories:

Results: The Six-Peril Crosswalk Every Risk Manager Should Memorize

The paper’s most immediately useful exhibit is a simple table — six perils, three columns — showing exactly which carriers have publicly positioned themselves against which AI threats.

The pattern is clear: the market is fragmenting by peril specialization, not by carrier size. No single carrier covers everything. Munich Re owns model drift. Armilla and Lloyd’s own hallucination. Coalition owns deepfake response. Apollo ibott owns agentic failure.

For a company deploying AI across multiple use cases, this means you need multiple endorsements from multiple carriers — or a comprehensive AI risk transfer review that identifies which perils your operations expose you to and which carriers are writing coverage for each.

The silent exposure problem is bigger than it looks. Legacy cyber insurance policies — the most common line companies rely on for AI-related losses — contain silent-AI exposure that neither side has properly characterized. A typical cyber policy may respond to an AI-caused data breach, but its applicability to AI-generated misinformation, algorithmic discrimination, or model theft has not been litigated or even seriously discussed between carrier and policyholder.

The exclusion trend is accelerating. Active exclusions — particularly in D&O, crime, and professional liability lines — are being added for management misconduct involving AI. If a CEO knowingly deploys an AI system that produces harmful outputs, the D&O policy is increasingly likely to exclude coverage. This aligns with the broader regulatory push toward senior management accountability for AI governance.

Key Takeaways for Chief Risk Officers and CFOs

The paper’s most important finding is one that does not fit neatly into any existing product category and should be on every board’s risk register.

For the Chief Risk Officer, this finding forces a question: if you rely on a single foundation model provider for critical operations, is that a risk you can retain? Or does it change your model diversification strategy from a technology decision to a balance sheet decision?

For the CFO, the question is different: does your insurance buyer know which of your AI use cases have coverage and which do not?

For the General Counsel, the question is disclosure. If a material AI-related loss occurs and the company discovers its insurance does not respond, was that a disclosure failure? The paper’s evidence that 8.9 percent of AI threat-by-product combinations sit in silent exposure suggests that in many companies, no one has asked the question.

Action Items for Every Company Deploying AI

Thanks to All Authors