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That distinction matters to businesses using AI—and to investors, customers and employees trying to understand who pays when an AI system makes a costly mistake.
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The short answer
Some insurers are reportedly seeking permission to exclude certain widespread AI-related liabilities from corporate policies. TechCrunch reported in November 2025 that Great American, Chubb and W. R. Berkley were associated with efforts to restrict broad AI exposure. AIG told TechCrunch it was not specifically seeking those exclusions and had no plans to implement them at that time. These reports do not mean that every insurer has stopped covering AI.
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The more accurate conclusion is:
- Bounded AI risks can be insured.
- Systemic AI risks are much harder to insure.
- Existing policies may be unclear rather than clearly protective or clearly excluding coverage.
- Specialist AI products are emerging, but they are underwritten case by case.
The insurance question is not simply whether a company uses AI. It is whether the company can show what the system was allowed to do, what it actually did, how the result can be verified, and how quickly the system can be stopped.
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Read TechCrunch’s reported account of the insurer debate.
What does it mean to insure AI?
“AI risk” covers several different exposures that may belong under different insurance policies.
1. Liability for AI developers
A company that builds or licenses an AI model or application could face claims alleging inaccurate outputs, privacy violations, copyright infringement, defamation, discrimination, negligent design or inadequate warnings.
2. Liability for AI deployers
A business can remain exposed even when a third-party model caused the immediate problem. Examples include an automated hiring system producing discriminatory results, a chatbot giving incorrect financial guidance, an agent sending an unauthorized payment or a model exposing confidential customer information.
3. AI-enabled cybercrime
Sometimes AI is not the source of the insured’s mistake; it is the attacker’s tool. Deepfake executive impersonation, AI-assisted phishing, credential attacks and prompt injection may fall under cyber or crime coverage if the policy responds to the resulting event. The wording matters.
4. AI performance risk
Some specialist products focus on whether a model performs below an agreed standard, even where the issue does not immediately produce a conventional liability claim. This is different from insuring a lawsuit caused by an AI output.
5. Systemic or aggregation risk
A single foundation model, cloud provider, software library or model update could affect thousands of customers at once. That shared exposure is one of the biggest challenges for traditional insurance.
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Why insurers are worried
Insurance works best when carriers have credible historical data and can diversify claims. AI complicates both assumptions.
- Frequency is uncertain: reliable claims histories for newer AI uses are limited.
- Severity varies dramatically: an error may require a corrected email—or trigger litigation, regulatory action, financial loss or physical harm.
- Losses may be correlated: the same faulty model or update can affect many insureds simultaneously.
- Responsibility is distributed: the model developer, application vendor, cloud provider, data supplier, deployer and employee may all play a role.
- Systems change during the policy period: models can be updated, fine-tuned, connected to new tools or granted more autonomy.
- Policy triggers may be unclear: one event could look like a cyber incident, professional error, product defect, privacy breach, media injury or none of these.
Correlation is the crucial issue. An AI system that makes occasional, independent mistakes may be priced. A shared model failure producing simultaneous claims across an industry is much harder to diversify.
The Geneva Association describes the dedicated AI-insurance market as nascent rather than nonexistent. Its survey covered 600 corporate insurance decision-makers or influencers across China, France, Germany, Japan, the United Kingdom and the United States. It reported that 71% had implemented generative AI in at least one function and that nine in ten showed interest in insurance for generative-AI risks. Those figures show demand—not guaranteed availability or affordability.
Three different failures that are often lumped together
A defamatory or inaccurate answer
TechCrunch reported that Google’s AI Overview falsely accused a solar company of legal troubles, prompting a lawsuit seeking $110 million. The reported claim and amount do not establish liability, insurance coverage or a payout. This is primarily an alleged inaccurate or defamatory-output problem.
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TechCrunch reported that Air Canada was required to honor a discount invented by its chatbot. This is a more bounded consumer-facing performance failure: the output was concrete, attributable and potentially measurable. It does not prove that a particular insurance policy would have paid.
Fraud using a deepfake
TechCrunch reported that criminals used a digitally cloned senior executive during a video call to induce a £25 million theft from engineering company Arup. That is more naturally viewed as AI-enabled social engineering or crime risk than as a model-liability claim. Coverage depends on policy language concerning deception, impersonation, funds transfers and authentication.
Are existing insurance policies automatically invalid when AI is used?
No. Using AI does not automatically void a policy. But it is equally unsafe to assume that a cyber, professional-liability or general-liability policy automatically covers every AI failure.
Coverage can depend on:
- the insuring agreement and definition of loss;
- whether a security failure or cyber event is required;
- whether the loss is first-party or third-party;
- professional-services, contractual-liability or intellectual-property exclusions;
- AI exclusions, endorsements, sublimits and aggregation provisions;
- what the company disclosed in its application;
- the specific allegation—negligence, breach of contract, privacy harm, defamation or discrimination; and
- whether the AI system was used within the described business activities.
This uncertainty is sometimes called silent AI: the policy was written without clearly addressing AI-specific failure modes. Armilla describes many legacy policies this way, but that is a product provider’s characterization, not a universal legal conclusion. Policy interpretation depends on the wording and applicable jurisdiction.
Before deployment, a company should ask its broker to identify every relevant exclusion, definition, endorsement, retention, sublimit and aggregation clause. A policy’s marketing label is not enough.
What insurers are likely to do
Businesses should expect a mixture of:
- Exclusions for specified AI-generated content, autonomous decisions, model errors or failure to perform.
- Sublimits that cap AI-related losses below the main policy limit.
- Narrow endorsements covering named applications, models, business processes or loss types.
- Higher retentions and premiums for autonomous or poorly documented systems.
- Underwriting conditions requiring testing, monitoring, logs, access controls, human approval and incident-response plans.
- Separate AI policies that supplement rather than replace cyber, crime, technology E&O, professional liability or general liability insurance.
Insurance regulation is also moving toward greater scrutiny of how insurers themselves use AI. The National Association of Insurance Commissioners’ AI materials address governance, risk mitigation, third-party model oversight and documentation. Its 2023 model bulletin is not automatically binding law in every state and is not a blanket requirement that commercial policies exclude AI.
What makes an AI risk more insurable?
| More insurable | Less insurable |
|---|---|
| Named model and version | Unknown or constantly changing providers |
| Limited autonomy and human approval | Agents able to move money or alter records without review |
| Measurable accuracy thresholds | Unclear definition of success or failure |
| Complete prompt, output, action and override logs | No audit trail explaining a decision |
| Version control, regression testing and drift monitoring | No testing after model updates |
| Restricted permissions and reversible actions | Unrestricted access to production systems |
| Defined vendor responsibilities and indemnities | Reliance on vague provider promises |
| Low aggregation and a meaningful retention | Identical high-volume deployments sharing one dependency |
A practical test is whether the organization can reconstruct the incident, show which control failed and demonstrate that the loss could have been limited. Insurers have more options when the risk has a defined trigger, a measurable boundary and evidence of control.
Questions to ask before buying coverage
- Is AI use affirmatively covered, silently covered or excluded?
- Are model errors and hallucinations covered without a cyberattack?
- Are autonomous agents treated differently from ordinary chatbots?
- Are first-party financial losses covered, or only third-party claims?
- How are privacy, copyright, defamation, discrimination and regulatory matters treated?
- Are vendor-, API- or foundation-model-caused failures covered?
- What happens after a model update or provider change?
- Are all incidents involving one model treated as one occurrence?
- Are there systemic-event exclusions or aggregation limits?
- Does coverage require human review, specified logs or insurer audit rights?
- Does crime insurance address synthetic impersonation and payment fraud?
- Do vendor indemnities count toward the company’s recovery, and are they enforceable?
What specialist products are emerging?
Affirmative AI liability
Armilla markets affirmative AI liability insurance for developers and deployers, alongside separate performance warranties and risk-assessment services. Its January 2026 announcement described limits of up to $25 million per organization. That is an announced capacity figure, not a guaranteed quote for every applicant; policy terms and underwriting conditions control.
Model-performance cover
The Geneva Association identifies Munich Re’s aiSure as a solution for AI providers and enterprise users, with pricing linked partly to model-error inputs. Exact availability, geography and terms should be confirmed directly or through a broker.
Outcome-based cyber cover
Coalition describes its Active Cyber Policy as responding to the outcome of an incident rather than the technology used and publicly advertises no AI exclusions in that product description. That does not replace reviewing the actual policy form, and it does not necessarily cover pure model underperformance with no cyber event.
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Companies can also transfer or retain risk through vendor indemnities, performance warranties, contractual liability caps, reserves, captives and industry pools. A warranty is not insurance: it is a contractual promise by a vendor, while insurance responds to covered losses subject to policy terms.
Technical controls often matter more than a specialist policy. Useful safeguards include human approval for consequential decisions, restricted tool permissions, dual authorization for payments, transaction limits, data minimization, adversarial testing, version pinning, rollback capability, fallback systems and continuous monitoring.
Insurance cannot make an unsafe AI system safe. It can transfer some financial consequences after a covered event, but it does not eliminate regulatory exposure, customer harm, operational disruption or reputational damage.
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What this means for smaller businesses
A bespoke AI policy may not be worthwhile for a small company using AI for low-impact drafting or internal productivity. Conventional cyber, crime or professional-liability insurance, stronger contracts and sensible controls may provide better value.
Specialist coverage becomes more relevant when AI affects customers, employment, credit, healthcare, financial decisions, revenue-critical workflows or autonomous transactions—or when enterprise customers require a vendor to carry affirmative AI liability insurance.
The appropriate decision is not “buy an AI policy” or “ignore insurance.” It is to map the particular use case across cyber, crime, technology E&O, professional liability, media liability, general liability and any specialist AI coverage, then identify the remaining uninsured loss.
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The insurance industry’s concern is not that every AI system is impossible to insure. It is that broad, opaque and interconnected AI exposure can generate correlated losses that conventional insurance was not designed to absorb.
Expect more exclusions for vague or systemic exposure, more underwriting conditions, more specialist products and greater pressure on companies to prove that their AI systems are bounded and controllable. For any business deploying AI, the most important question is:
For this named system, with these permissions, controls, contracts and policy terms, which losses remain our responsibility?
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