<\/p>\n
<\/p>\n
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.<\/p>\n
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.<\/p>\n
This paper fills that gap at industrial scale. The authors constructed a 55-by-26 matrix \u2014 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.<\/p>\n
The result is the first evidence-based map of where the AI insurance market actually stands. And it reveals a market in active bifurcation \u2014 splitting into specialized affirmative products while legacy lines accumulate silent exposure that almost no one has quantified.<\/p>\n
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?”<\/em><\/p>\n The answer, in most cases, is “we do not know \u2014 and here is how to find out.”<\/em><\/p>\n <\/p>\n 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?<\/p>\n That is the state of AI insurance today.<\/p>\n The authors took two giant lists and cross-referenced them. The first list was 55 AI threat classes \u2014 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).<\/p>\n The second list was 26 commercial insurance products \u2014 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.<\/p>\n For each of the 1,430 combinations, the authors coded the market position into one of four categories:<\/p>\n <\/p>\n An insurer has publicly stated<\/strong> that a specific endorsement or product covers this threat. This includes AI-specific products like Munich Re’s aiSure and Armilla’s ALAS warranty.<\/p>\n<\/p>\n<\/div>\n The threat sits within existing legacy policy wording<\/strong>, but neither the carrier nor the policyholder has explicitly tested whether it would respond. The paper estimates 8.9% of threat-by-product combinations<\/strong> fall here. For example: an AI agent makes a hiring decision that discriminates against a protected class. Would your EPLI policy respond? Possibly. Has your carrier confirmed it? Probably not.<\/p>\n<\/p>\n<\/div>\n Carriers have added explicit carve-outs<\/strong> for AI-caused losses. AI-washing by corporate management, intentional deception using AI, and knowing misconduct are increasingly excluded from D&O and crime policies.<\/p>\n<\/p>\n<\/div>\n The threat has no plausible connection<\/strong> to the product. A model hallucination has nothing to do with commercial property insurance.<\/p>\n<\/p>\n<\/div>\n<\/div>\n <\/p>\n Among Addressable AI Risks<\/p>\n How the U.S. Commercial Insurance Market Responds<\/p>\n Based on 1,430 threat-product combinations. More than a quarter of the addressable AI risk landscape sits in a gray zone that no one has tested.<\/p>\n<\/div>\n <\/p>\n The paper’s most immediately useful exhibit is a simple table \u2014 six perils, three columns \u2014 showing exactly which carriers have publicly positioned themselves against which AI threats.<\/p>\n 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.<\/p>\n For a company deploying AI across multiple use cases, this means you need multiple endorsements from multiple carriers \u2014 or a comprehensive AI risk transfer review that identifies which perils your operations expose you to and which carriers are writing coverage for each.<\/p>\n The silent exposure problem is bigger than it looks. Legacy cyber insurance policies \u2014 the most common line companies rely on for AI-related losses \u2014 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.<\/p>\n The exclusion trend is accelerating. Active exclusions \u2014 particularly in D&O, crime, and professional liability lines \u2014 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.<\/p>\n <\/p>\n 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.<\/p>\n <\/p>\n Critical Structural Gap<\/p>\n Threat T-45 in the paper’s classification describes a scenario where a single upstream foundation model failure \u2014 a catastrophic update from OpenAI, Anthropic, Google, or Meta; a supply-chain compromise; a data contamination event \u2014 triggers losses for hundreds of downstream companies simultaneously<\/strong>. The exposure resembles an earthquake: correlated, sudden, and enormous in aggregate. But unlike earthquakes, there are no actuarial models for this risk and no public insurance product<\/em> designed to absorb it. The paper identifies this as the “clearest genuinely novel insurability frontier.”<\/p>\n<\/div>\n For the Chief Risk Officer<\/strong>, 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?<\/p>\n For the CFO<\/strong>, the question is different: does your insurance buyer know which of your AI use cases have coverage and which do not?<\/p>\n For the General Counsel<\/strong>, 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.<\/p>\n <\/p>\n Run the paper’s threat classification against your AI use cases<\/p>\n Which of the 55 threat classes are relevant to your operations? The OWASP and MITRE taxonomies the paper uses are public and well-documented.<\/p>\n<\/p>\n<\/div>\n<\/div>\n Audit your current insurance portfolio against those threats<\/p>\n For each relevant threat class, determine whether your existing policies provide affirmative coverage, silent exposure, or active exclusion. This audit does not require an actuary \u2014 it requires reading your policy wording against the paper’s framework.<\/p>\n<\/p>\n<\/div>\n<\/div>\n Estimate your silent-AI exposure<\/p>\n The paper finds 8.9% of cells sit in silent exposure, but the distribution is uneven. Cyber and tech E&O carry the heaviest silent burden. D&O and EPLI are rapidly adding exclusions.<\/p>\n<\/p>\n<\/div>\n<\/div>\n Evaluate foundation model concentration risk<\/p>\n If your operations depend on a single upstream model provider, quantify the correlated loss exposure. This risk may not be insurable in any existing market. The only mitigant is operational: diversify your model providers, build fallback architectures, and maintain the ability to operate without the primary model for extended periods.<\/p>\n<\/p>\n<\/div>\n<\/div>\n Engage your broker with a specific AI risk transfer brief<\/p>\n Do not ask “do we have AI coverage” \u2014 the answer is almost certainly no. Instead, ask: “For our specific AI use cases, which carriers write affirmative coverage for which perils, and what is the premium for bridging our silent exposure?”<\/p>\n<\/p>\n<\/div>\n<\/div>\n<\/div>\n <\/p>\n \n Alex Leung \u2014 No institutional affiliation listed Your existing insurance policies have gaps that create material uninsured liability for AI-specific losses \u2014 the paper’s 8.9% silent-exposure rate means nearly 1 in 11 risk scenarios fall through coverage cracks without your knowledge. You now have a quantified map to demand either new affirmative AI coverage or explicit policy amendments from your broker before an incident forces a coverage denial.<\/p>\n<\/div>\n The insurance market has not yet developed products that cover losses stemming from third-party foundation model failures \u2014 whether through hallucinations, data poisoning, or model collapse \u2014 because carriers cannot price the systemic concentration risk across the industry. This means your organization bears unshifted liability for AI failures that originate outside your control, making vendor AI risk management and contractual indemnification your only current defenses.<\/p>\n<\/div>\n Silicon Valley Certification Hub’s analysis provides the first evidence-based framework to identify which of your 55 classes of AI threats are actually insurable under your current policy stack, allowing you to prioritize both insurance procurement and internal risk controls where coverage gaps exist. This transforms AI Assessment from a compliance checklist into a financial risk quantification exercise tied directly to your carrier agreements.<\/p>\n<\/div>\n Schedule an insurance review with your broker within 60 days that explicitly uses this 55-threat taxonomy to audit your current policies for AI-caused loss response \u2014 silence from your carrier on a specific threat class should be treated as a coverage exclusion. Simultaneously, map your own AI deployment roadmap against the uninsurable frontier and decide whether to reduce exposure in those areas, self-insure, or accept the liability outright.<\/p>\n<\/div>\n<\/div>\n <\/p>\nMethodology, Explained Simply<\/h2>\n
Results: The Six-Peril Crosswalk Every Risk Manager Should Memorize<\/h2>\n
\n\n
\n \nAI Peril<\/th>\n Carrier \/ Product<\/th>\n Status<\/th>\n<\/tr>\n<\/thead>\n \n Model Drift \/ Performance<\/td>\n Munich Re \u2014 aiSure<\/em><\/td>\n AFFIRMATIVE<\/span><\/td>\n<\/tr>\n \n Hallucination-Related Losses<\/td>\n Armilla, Chaucer, Hiscox<\/td>\n AFFIRMATIVE<\/span><\/td>\n<\/tr>\n \n IP Infringement from AI Outputs<\/td>\n Tokio Marine Kiln, CFC<\/td>\n AFFIRMATIVE<\/span><\/td>\n<\/tr>\n \n Autonomous \/ Agentic System Failure<\/td>\n Apollo ibott \u2014 Syndicate 1971<\/em><\/td>\n AFFIRMATIVE<\/span><\/td>\n<\/tr>\n \n Deepfake Incidents<\/td>\n Coalition \u2014 Incident Response<\/em><\/td>\n AFFIRMATIVE<\/span><\/td>\n<\/tr>\n \n Foundation Model Concentration<\/td>\n No public product identified<\/td>\n NO COVERAGE<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n Key Takeaways for Chief Risk Officers and CFOs<\/h2>\n
Foundation Model Concentration Risk Has No Insurance Answer<\/h3>\n
Action Items for Every Company Deploying AI<\/h2>\n
Thanks to All Authors<\/h2>\n
\n Rex Zhang \u2014 No institutional affiliation listed
\n Ervin Ling \u2014 No institutional affiliation listed
\n Kentaroh Toyoda \u2014 No institutional affiliation listed
\n SiewMei Loh \u2014 No institutional affiliation listed\n <\/p>\n<\/div>\nFrequently Asked Questions<\/h2>\n
What does this mean for a Chief AI Officer?<\/h3>\n
How should we interpret the finding that foundation model concentration is ‘uninsurable’?<\/h3>\n
How does this paper support an AI Assessment for companies evaluating their risk posture?<\/h3>\n
What should our executive team do with this map before year-end?<\/h3>\n