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Coverage guide
AI Liability Insurance
Dedicated coverage for the unique risks created by artificial intelligence, from model errors and hallucinations to algorithmic discrimination and autonomous decision-making failures. This guide explains what it covers, how it differs from the exclusions now appearing in other policies, what drives the price and what underwriters want to see.
The short version
- What it is
- Insurance that responds when someone claims your AI product, model or automated decision caused them harm: a wrong output, a biased decision, an IP dispute or a regulatory inquiry.
- Who needs it
- Companies that build AI products, embed third-party models in what they sell, or use automated decisions that affect customers, applicants or patients.
- Biggest pitfall
- Assuming your tech E&O or cyber policy already covers AI. Many forms now carry exclusions broad enough to reach any claim involving machine learning, and others stay silent, which leaves the answer to a coverage dispute.
- What drives price
- Use case, how much a human reviews outputs, training data sources, deployment context, revenue, contract terms and the limits you buy.
- Typical spend
- Early-stage AI companies typically start at $5,000 to $25,000 per year. Higher-risk applications such as healthcare, autonomous systems and financial decisioning pay more.
The basics
What is AI liability insurance, in plain English?
Traditional E&O and GL policies were not designed for the risks AI creates. Model hallucinations, training data bias, autonomous decisions that cause harm, and regulatory enforcement under evolving AI governance frameworks all fall into gaps that standard policies don’t address. AI liability insurance is built for the claims that arise from how an AI product behaves, and coverage varies a lot by carrier and is still developing, so the wording matters more than the label.
AI liability insurance pays defense costs, settlements and judgments when a third party says your AI caused them a loss. It is a third-party liability cover. It responds to what other people claim against you, not to what an AI failure costs you directly. In practice it is usually written as an affirmative AI grant inside a tech E&O program or as a standalone policy, and sometimes both. See the glossary entry for AI liability insurance.
We work with the small number of carriers globally who are actively writing AI-specific liability coverage, and we know how to present your model governance, testing frameworks and human oversight processes in the language underwriters need. Where standard forms fall short, we negotiate and manuscript policy language so coverage matches how your models actually work.
For venture-backed companies it is also a sales document. Enterprise customers increasingly ask AI vendors for proof of coverage during security and procurement review, and a vague answer slows deals. Read it alongside the full AI liability guide on our blog.
Silent vs. affirmative
Is AI covered by my existing policies, or is it silent?
Short answer: it depends on the wording, and often nobody knows until a claim is filed. A policy can exclude AI, say nothing about it, or grant it expressly. Only the last gives you a clear answer in advance.
Insurers use the term “silent AI” for the same reason they once used “silent cyber.” Older forms never mentioned machine learning, so a claim involving a model could arguably fit under the general promise to cover errors in technology services. As carriers reassess that exposure, many are adding explicit language. Three outcomes are now common.
Exclusionary
The form carves out claims arising from artificial intelligence, machine learning or automated decision-making, sometimes in a single broad sentence and sometimes tied to specific uses.
Why it matters: a broad exclusion can remove the very claims an AI company is most likely to face. Read how far “arising from” reaches.
Silent
The policy neither grants nor excludes AI. Coverage turns on how the insuring agreement and definitions apply to a model-driven service.
Why it matters: the insurer and the insured may read the same words differently, and you find out during a claim, which is the worst moment.
Affirmative
The policy expressly states that covered technology services include AI or machine learning outputs, and defines what AI-related events respond.
Why it matters: this is the only version you can price, plan around and show a customer with confidence.
Partial or sublimited
AI is covered for some exposures, such as errors, but a lower limit or retention applies to bias, regulatory or IP claims.
Why it matters: it is a reasonable compromise when the sublimit matches your real exposure, and a trap when it does not.
Rule of thumb
Do not assume silence means coverage, and do not assume an exclusion means you are uninsurable. Ask for the AI wording in writing, then decide whether to negotiate it out, endorse AI back in or add a separate policy.
Who needs it
Who needs AI liability insurance, and when?
Short answer: any company whose product, service or internal process relies on AI output that a customer or third party might rely on, and the need grows with how consequential those outputs are. This guide is general, and your lawyers can tell you where specific legal duties apply.
Prototype
Internal testing, no outside users yet. Low exposure.
PlanFirst customers
Real users rely on outputs. Contracts start to mention insurance.
Review exclusionsEnterprise deals
Procurement asks for AI-specific proof and indemnity terms.
Buy nowConsequential use
Outputs drive money, health or hiring decisions.
Higher limitsScale or exit
More users, more claims, and diligence on both sides.
Revisit yearlyRule of thumb
If a customer could lose money, a job, a loan or a diagnosis because your model was wrong, treat AI liability as a product requirement, not a nice-to-have.
| Where you are | Do you need it? | Why |
|---|---|---|
| Customer-facing chatbot or assistant | Yes, once real users rely on it | Wrong answers, invented facts or off-brand statements can turn into misrepresentation or negligence claims. Disclaimers help but rarely settle the matter. |
| Code, content or image generation tool | Yes, with IP in focus | Customers and rightsholders may allege that outputs or training data infringe. Check how the policy treats IP. |
| Automated decisions about people (hiring, lending, insurance, tenant screening) | Yes, with bias and regulatory cover | These are the uses most likely to draw discrimination allegations and regulatory attention. Confirm bias claims are not excluded. |
| Tools in healthcare, legal, financial or tax settings | Yes, expect more underwriting | Higher stakes mean closer questions about human review, testing and scope of advice. Professional liability wording matters. |
| Autonomous agents that take actions (payments, bookings, code deploys) | Yes, and read the definitions | An agent acting wrongly can create losses with no human decision in the loop. Some forms are unclear on whether that is a covered service. |
| Model builder or API provider | Yes, often a custom placement | You sit upstream of many customers. Aggregation and flow-down claims worry carriers, so expect detailed questions. |
| Company embedding a third-party model in its product | Yes, you own the customer relationship | Customers will look to you first, even if the model provider is partly to blame. See enterprise contract insurance requirements. |
| Physical AI, robotics or devices with embedded models | Yes, alongside product liability | Bodily injury and property damage usually sit on other policies. See physical AI and hardware. |
Scope
What AI liability insurance covers, and what it doesn’t
Short answer: it typically covers third-party claims arising from your AI-enabled products or services, plus defense costs, and it typically leaves out physical injury, your own first-party losses and deliberate misconduct. Coverage varies a lot by carrier, so treat these lists as a starting point.
What’s Covered
- Model errors and hallucinations that cause a customer financial loss
- Algorithmic bias and discrimination claims arising from automated decisions
- Intellectual property claims tied to training data or outputs, subject to the wording
- Regulatory investigations and defense costs, usually with sublimits, plus fines where insurable by law
- An AI-enabled service failing to perform as promised
- Data privacy violations connected to how a model handles personal data, depending on the policy
What’s NOT Covered
Most AI liability policies exclude:
- Bodily injury and property damage, which belong on product or general liability
- Your own first-party costs, such as responding to a breach, which belong on cyber
- Deliberate fraud or knowing violations of law, once established
- Employment claims such as wrongful termination, unless EPL is purchased
- Claims arising from facts known before the policy began
- Refunds of fees or the cost of fixing your own model, in many forms
Regulatory fines and penalties are often limited. Where they are covered at all, it is usually only where the law allows them to be insured and the policy says so. Ask for the specific wording and any sublimit.
Where claims come from
What kinds of AI claims actually arise?
Short answer: most AI claims fall into six buckets: wrong outputs, intellectual property, bias, privacy, regulatory scrutiny and actions taken by autonomous systems. Each behaves differently under a policy, so it helps to know which one your product is most exposed to.
Wrong outputs and hallucinations
A model states something false or gives a poor recommendation, and a customer relies on it and loses money or time.
Why it matters: this is closest to classic E&O. The question is whether the policy treats generated output as a covered technology service.
IP and copyright
A rightsholder alleges that training data was used without permission, or that outputs reproduce protected material.
Why it matters: many policies limit or exclude IP, and patent claims are often treated separately. See our note on patent infringement liability for emerging tech.
Bias and discrimination
Someone alleges that an automated screening, scoring or pricing decision treated them unfairly because of a protected characteristic.
Why it matters: these claims can come from individuals, groups or regulators, and some forms exclude them unless you ask.
Regulatory exposure
A regulator asks questions about how your system makes decisions, what it told consumers or how it was tested. Rules vary by jurisdiction and are changing.
Why it matters: defense costs for inquiries are often the first thing paid and the thing most likely to be sublimited.
Autonomous agents and automated decisions
A system takes an action, such as a purchase, a message or a change to code, without a person approving it.
Why it matters: the less human review there is, the more underwriters ask about guardrails, and the more the policy definitions matter.
Your role in the chain
Model builder, deployer or reseller: how your role changes the risk
Short answer: the closer you are to the customer, the more likely you are to be the first name on a claim, even if the model came from someone else. Underwriters therefore ask about your role before anything else.
| Your role | Typical exposure | What underwriters focus on |
|---|---|---|
| Model builder or foundation model provider | Training data rights, harmful outputs at scale, claims from many downstream customers at once. | Data provenance, safety testing, usage policies and how liability is capped in your terms. |
| Application builder on a third-party model | Customer claims about outputs, plus gaps between what your provider promises you and what you promise your customers. | Provider contract terms, your own testing, output filtering and your customer agreements. |
| Company using AI inside its own operations or decisions | Claims from people affected by automated decisions, and regulatory questions about fairness and transparency. | Human review, appeals processes, documentation and the kinds of decisions automated. |
| Reseller, integrator or consultant | Claims that you chose, configured or sold an AI tool that did not perform as represented. | What you promise, how you test before delivery and the contracts that sit above and below you. |
Rule of thumb
Do not assume your model provider will carry the loss. Provider terms often cap liability, may offer only limited IP protection and rarely cover your customers’ downstream claims, so read the flow-down before you rely on it.
Which policy responds?
AI liability vs. the coverages it’s confused with
AI claims rarely arrive labeled. Here is how common situations usually map to the policies an AI company carries. The answers depend on the actual wording, so read “covered” as “often responds if the form allows it.”
| Scenario | AI liability | Tech E&O | Cyber | D&O |
|---|---|---|---|---|
| A customer loses money after relying on a wrong model output | Covered, if AI is affirmed | Maybe, if no AI exclusion | No | No |
| A rightsholder alleges training data or outputs infringe their copyright | Covered, if IP is not excluded | Often limited or excluded | No | No |
| An applicant alleges an automated screening tool discriminated | Covered, if bias claims are included | Often excluded or silent | No | No |
| A chatbot exposes one user’s personal data to another | Usually the cyber policy’s job | No | Covered, subject to terms | No |
| A regulator opens an inquiry into automated decisions | Defense costs, often sublimited | Rarely | Only privacy-related | For individuals, if investigations are covered |
| Investors allege the company overstated its AI capabilities | No | No | No | Covered, subject to terms |
| An AI-controlled device injures someone, which usually belongs on product or general liability | No | No | No | No |
How it is built
How AI liability fits with tech E&O and cyber
Short answer: there are three ways to buy it: an affirmative AI endorsement on your tech E&O, an AI coverage part inside a combined tech E&O and cyber program, or a standalone AI liability policy. Which is best depends on how much AI sits at the center of what you sell.
| Approach | How it works | Trade-off |
|---|---|---|
| Endorsement on tech E&O | The carrier adds language that confirms AI-related services are covered, or removes an exclusion. | Simple and usually cheaper, but you share limits with every other tech E&O claim and the wording may be narrow. |
| AI coverage part in a combined program | Tech E&O, cyber and AI exposures sit in one form with defined AI insuring language. | Coordinated and easier to certify, though few carriers offer it and sublimits can apply. |
| Standalone AI liability policy | A separate policy written around AI risks, with its own limit and conditions. | Clearer cover and a dedicated limit, at the cost of another premium and another application. |
It also matters how the policies work together. An AI-driven data leak may involve cyber for response costs, AI liability for third-party claims about the model and D&O if investors later sue over what leadership said. A good program makes sure the pieces do not contradict each other. Read our overview of errors and omissions if you are new to the base policy.
How much limit should we buy?
Benchmark against what customers require, how much a single wrong output could cost them and what you can afford to retain. Enterprise contracts often set the floor.
Why it matters: a limit that meets the contract but not the realistic loss leaves you exposed. See limit of liability.
What retention fits?
The retention is what you pay before the insurer does. Higher retentions lower premium but must be money you can actually fund. See retention.
Why it matters: a retention you cannot afford turns a covered claim into an uncovered one in practice.
Read the fine print
Claims-made, retroactive dates and the definitions that decide a claim
Short answer: AI liability is almost always claims-made, so the policy in force when the claim is first made responds, and the retroactive date decides how far back it looks. A model you shipped years ago can generate a claim today.
A model ships
You deploy a model or feature in a year when Policy A is in force.
Year 1Coverage changes
Policy A ends and Policy B begins, or cover lapses.
Year 2A claim arrives
The policy in force now responds, if its retroactive date reaches back to Year 1.
Year 3Rule of thumb
Do not let AI cover lapse, and do not accept a retroactive date later than your first deployment. A new retro date can leave older models uncovered.
Claims-made trigger
Cover applies to claims first made and reported during the policy period, not to events that merely happened then.
Why it matters: late reporting can forfeit a valid claim, so know your notice duties.
Retroactive date
The earliest date of an act or error the policy will consider. Older models fall outside if the date is later.
Why it matters: ask for full prior acts or a date at or before your first launch.
Definition of “claim”
Determines whether demand letters, regulatory inquiries and requests for information trigger cover or only lawsuits.
Why it matters: AI disputes often begin with a letter or a regulator’s question, not a complaint.
Definition of “wrongful act”
The covered conduct, such as an error in providing technology services. See wrongful act.
Why it matters: it should be broad enough to include AI-generated output and automated actions.
Definition of technology services
Describes the products and services the policy protects, and whether AI features are named.
Why it matters: if your newest feature is outside the definition, it may sit outside the cover.
Extended reporting period
Lets you report claims after the policy ends. See ERP.
Why it matters: valuable if you sunset a product, change carriers or are acquired.
Ask for these
Policy features worth asking for when you buy AI cover
Two AI policies with the same limit can respond very differently. These are the points we raise most often, in plain terms, and each is an endorsement or wording change you can request.
Narrow or remove the broad AI exclusion
Replace a sweeping exclusion with one tied to specific, defined uses.
Why it matters: a narrow exclusion leaves most real claims in scope.
An affirmative AI grant
Language that states AI and machine learning outputs are covered technology services.
Why it matters: it ends the silent versus excluded debate in advance.
Regulatory defense cover
Pays to respond to inquiries and enforcement, ideally including informal requests, with a stated sublimit.
Why it matters: inquiries usually come before any lawsuit and can be costly.
IP cover that fits how you train and ship
Cover for claims tied to training data and outputs, with exclusions limited to knowing violations.
Why it matters: a blanket IP exclusion can remove the claim you worry about most.
Bias and discrimination claims included
Confirms that allegations of unfair automated decisions are covered, not only technical errors.
Why it matters: for hiring, lending and screening tools, this is the core exposure.
Notice of circumstances
Lets you report a situation that may become a claim and lock it into the current policy.
Why it matters: if cover changes next year, an early notice can protect you.
When things change
What changes over time: growth, renewal and claims
Short answer: AI coverage needs attention at product launches, model changes, new customer types and every renewal, because the exposure moves faster than most policy terms. Here is how the common moments usually play out.
| What happens | What to do |
|---|---|
| You launch a new model or major feature | Tell your broker before launch. Confirm it falls inside the definition of covered services and that the retro date reaches it. |
| You switch model providers or add one | Review provider contracts again. Indemnities and limits may differ, and underwriters will ask. |
| Renewal approaches | Update the submission with your testing, governance and incident history. Carriers are changing AI wording often, so ask what changed. |
| A complaint, demand letter or incident occurs | Report it promptly and consult counsel. Read the notice requirements before you respond. |
| You are acquired or wind down | Ask about extended reporting or run-off so claims about earlier models still have a policy. |
What claims look like
Claim scenarios
Scenario 01
The confident wrong answer
What happenedA company offered an AI assistant that summarized contracts for small businesses. One summary missed a renewal clause, and a customer alleged it lost money as a result.
The claimNegligence and breach of contract, based on reliance on the output.
What respondedIt depended on whether the tech E&O form treated generated summaries as covered services or excluded AI. An affirmative grant answered the question.
Scenario 02
The screening tool
What happenedA hiring platform used a scoring model to rank applicants. A group of applicants alleged the tool disadvantaged them.
The claimA discrimination allegation against the platform and its customers.
What respondedCover for bias claims, if included, paid defense. A form that excluded algorithmic discrimination left the company to fund it.
Scenario 03
The training data dispute
What happenedA creator group alleged that a company trained its model on protected works without permission.
The claimCopyright infringement relating to training data and generated outputs.
What respondedOnly a policy with IP cover that reached training activity and a retro date before the training began.
Scenario 04
The agent that acted alone
What happenedAn autonomous purchasing agent placed orders outside its intended limits for several customers before anyone noticed.
The claimCustomers sought reimbursement and alleged the service failed to perform as promised.
What respondedCoverage turned on whether agent actions counted as technology services and on any exclusion for unattended automated activity.
Scenario 05
The regulator’s questions
What happenedA regulator sent a request for information about how an automated eligibility tool reached its decisions.
The claimA regulatory inquiry with no lawsuit filed.
What respondedRegulatory defense cover, if the policy defined claims to include inquiries, usually under a sublimit.
Scenarios are illustrative composites based on common claim patterns, not specific Alton Risk clients.
Pricing
How much does AI liability insurance cost, and what moves the price?
Premiums vary widely based on AI use case, training data, deployment context and revenue. Early-stage AI companies typically start at $5,000 to $25,000 per year. Companies with high-risk AI applications (healthcare, autonomous systems, financial decisioning) pay more. Two companies at the same stage can still land far apart, because underwriters are pricing how consequential a wrong output would be.
| Factor | Why it moves your premium |
|---|---|
| Use case and consequence | Outputs that influence health, money or employment decisions cost more to insure than low-stakes tools. |
| Human review | A person reviewing high-impact outputs before they reach customers is among the strongest signals of lower risk. |
| Training data | Clear licensing and documented provenance reduce IP concerns. Scraped or unclear data raises them. |
| Deployment context | Consumer-facing, regulated or autonomous deployments draw more scrutiny than internal tools. |
| Revenue and customer count | More customers and more revenue mean more potential claimants and larger losses. |
| Contracts and disclaimers | Liability caps, clear terms and realistic claims about capability make a submission stronger. |
| Limits and retention | Higher limits cost more, though each added layer is usually cheaper than the first. A higher retention lowers premium. |
Getting covered
How to get AI liability quoted and bound without delay
Straightforward companies can often be quoted quickly. AI risks usually take longer, because fewer carriers write them and each wants to understand the product. Having the information underwriters ask for ready is the biggest thing you control.
What to have ready
- A plain description of what your AI does and who relies on it.
- Which models you build, fine-tune or license, and the provider contracts.
- Where training data comes from and how it is licensed.
- How outputs are tested, monitored and reviewed by people.
- Your customer terms, disclaimers and any liability caps.
- Your existing tech E&O, cyber and D&O policies, with their AI wording.
- Any incidents, complaints, demand letters or regulatory contact.
What delays a bind most often?
A vague product description, missing provider contracts, an unmentioned complaint and an application filed the week an enterprise deal closes.
Why it matters: each is avoidable with a short review before you start.
What mistakes cost companies the most?
Assuming existing E&O covers AI, comparing premiums without comparing exclusions and letting a policy lapse.
Why it matters: the cheapest quote is the one most worth reading line by line.
Is a certificate of insurance the same as the policy?
No. A certificate summarizes cover, while the policy and endorsements govern a claim.
Why it matters: customers may ask for the AI wording itself, so request it early.
From the carrier’s side of the table
What underwriters ask before they quote
Having underwritten these risks on the carrier side, we know an AI submission is won or lost on how clearly it shows control over the model. A well-prepared submission gets more carriers interested, and more interest is what moves price and terms.
- Model governance and testing practices, including how you evaluate accuracy, bias and safety before and after release.
- Human oversight of high-impact decisions, and whether people can override or appeal automated outcomes.
- Where your training data comes from and how it is licensed, with attention to consent and any personal data.
- Your contracts with model providers, including indemnities and limits.
- Customer-facing terms and disclaimers, and whether marketing claims match what the product can do.
- Monitoring and incident response, including how you detect drift, handle complaints and roll back a faulty model.
- The scope of autonomy, meaning which actions the system can take without approval and what limits it has.
Self-audit
Seven questions to ask about your current coverage
- Does your tech E&O or cyber policy contain an AI or machine learning exclusion, and have you read exactly how it is worded?
- If the policy is silent on AI, have you asked the carrier in writing how it would respond?
- Does the definition of technology services include AI-generated output and automated actions?
- Are bias, IP and regulatory claims covered, sublimited or excluded?
- Does the retroactive date reach back to your first deployed model?
- Do your limits and retention match your customer contract requirements and your realistic worst case?
- Do you know what your model providers’ contracts do and do not cover?
If you can’t answer yes to all seven, send us your policy and we will review it line by line.
Who needs it
Industries that need AI Liability
AI & Emerging Tech →
Venture-Backed Startups →
Digital Assets →
Data Centers →
Physical AI & Hardware →
Key terms to know
AI Liability Insurance →
Technology E&O (Tech E&O) →
Claims-Made Policy →
Retroactive Date →
Insurance Exclusion →
Wrongful Act →
Cyber Liability Insurance →
Sublimit →
→ AI liability insurance: what it covers, the exclusions to watch, and who needs it
→ Why emerging tech companies can’t ignore patent infringement liability
→ 6 reasons high-growth companies should buy D&O coverage now
Related reading
AI liability insurance: the full guide →
Patent Infringement Liability for Tech →
Insurance in Enterprise Contracts →
D&O for High-Growth Companies →
Duty to Defend vs. Duty to Indemnify →
Related coverages
Frequently Asked
Common questions
What is AI liability insurance?
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AI liability insurance covers claims arising from AI model errors, hallucinations, algorithmic discrimination, biased outputs, and regulatory enforcement actions. It differs from traditional Tech E&O because most E&O policies now exclude AI. Alton Risk places policies that explicitly cover AI risks.
Do I need AI insurance if I have Tech E&O?
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Probably yes. Many Tech E&O policies now contain AI exclusions broad enough to deny any claim involving machine learning or automated decision-making. Alton Risk scans every policy for these exclusions and either negotiates them out or adds a standalone AI liability policy.
How much does AI liability insurance cost?
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Premiums vary widely based on AI use case, training data, deployment context, and revenue. Early-stage AI companies typically start at $5,000 to $25,000 per year. Companies with high-risk AI applications (healthcare, autonomous systems, financial decisioning) pay more.
What AI risks does the insurance cover?
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Coverage typically includes model performance failures, hallucinations causing financial harm, discrimination or bias in automated decisions, data privacy violations, intellectual property claims on training data, and regulatory fines where insurable by law.
What is “silent AI” in an insurance policy?
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It means a policy neither covers nor excludes AI, so it is unclear whether a model-related claim is covered. The answer then depends on how the policy’s definitions apply, which is often decided only after a claim. Asking the carrier to confirm in writing, or adding affirmative language, removes the uncertainty.
Does Tech E&O cover AI hallucinations?
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It depends on the wording. If the policy has no AI exclusion and treats generated output as a covered technology service, a wrong-answer claim may respond. If it carries a broad AI exclusion, it may not. Read the exclusions and the definition of technology services before assuming either way.
Does cyber insurance cover AI claims?
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Cyber usually responds to security events, data breaches and related response costs. It generally does not respond to claims that a model gave a bad answer or made a biased decision. A data exposure caused by an AI system may touch both cyber and AI liability, so the two should be read together.
Is copyright infringement from AI training data covered?
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Sometimes. Many policies limit or exclude intellectual property claims, and training-data disputes are a point carriers examine closely. Coverage typically depends on the IP wording, any knowing-violation exclusion and the retroactive date. Underwriters will ask where your data came from and how it is licensed.
Are bias and discrimination claims covered?
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They can be, if the policy includes them. Some forms exclude algorithmic discrimination or treat it narrowly. For hiring, lending and screening tools, confirm that allegations of unfair automated decisions are expressly covered and that regulatory defense for those claims is addressed.
Are regulatory fines and investigations covered?
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Defense costs for regulatory inquiries are often covered under a sublimit, depending on how the policy defines a claim. Fines and penalties are typically covered only where the law allows them to be insured and the policy says so. Rules vary by jurisdiction, and counsel can advise on specifics.
Who is liable if I build on a third-party model?
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Customers will usually look to the company they contracted with, even if the model provider contributed to the problem. Provider contracts often cap liability and may offer limited protection. Whether and how you can pass loss upstream is a legal question, and the cover you buy should not depend on it.
Do autonomous AI agents change the coverage picture?
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They can. When a system acts without a person approving each step, policy definitions matter more, and underwriters ask about guardrails, spending limits and monitoring. Confirm that agent actions fall within covered technology services and that no exclusion applies to unattended automated activity.
What do underwriters ask about AI governance?
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Typically how you test accuracy and bias, whether humans review high-impact outputs, where training data comes from, what your model provider contracts say, how you monitor and respond to incidents, and what your customer terms and disclaimers promise. Clear, documented answers help price and terms.
Is AI liability insurance claims-made, and does the retroactive date matter?
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It is usually claims-made, so the policy in force when a claim is first made responds. The retroactive date sets how far back covered acts can go, so a date later than your first deployment can leave older models uncovered. Keep coverage continuous and ask for a date that matches your launch.
Will AI coverage satisfy my customers’ contract requirements?
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It can, if limits, additional insured and other required terms match the contract. Enterprise buyers increasingly ask AI vendors about coverage, and some name AI explicitly. We read the contract language and compare it with your policy before certificates are issued, so paperwork does not stall a deal.
Can't find an answer to your questions? Reach out to our team →
Sources and further reading: NIST, "AI Risk Management Framework" (opens in a new tab); Independent Insurance Agents & Brokers of America: Verisk generative AI exclusions (opens in a new tab); Claims Journal: Insurer interest in AI exclusions growing (opens in a new tab); CSIS: The insurance industry's retreat from AI (opens in a new tab); IRMI: Technology errors and omissions insurance (opens in a new tab); Lockton: Understanding technology errors and omissions coverage (opens in a new tab); IRMI: Exclusion (opens in a new tab); IRMI: Exception (to exclusion) (opens in a new tab); IRMI: Insured versus insured exclusion (opens in a new tab). This page is general information, not legal or insurance advice. Coverage terms vary by carrier, policy form and state.