Does cyber insurance cover a claim caused by an AI feature?

Only where the claim also fits a cyber trigger, and most AI claims do not. A cyber policy responds to unauthorised access, data compromise, extortion, and business interruption from a security event. An AI model that summarises a contract wrongly, quotes a price it was never authorised to quote, or tells a customer something false has caused a loss without any security failure at all. If the same incident also leaked personal data — a model surfacing another customer's record, or a prompt log retaining sensitive input — then a cyber trigger may attach to that part of the loss and not to the rest. Read the trigger language rather than the marketing summary.

What is an absolute AI exclusion and how do I spot one?

It is an endorsement that removes cover for any claim arising out of, based upon, or attributable to the use, development or output of artificial intelligence. The phrase to look for is arising out of — it is deliberately broad and courts read it broadly, so it can reach a claim whose proximate cause was human even if AI touched the workflow. Narrower versions exist and are worth negotiating for: exclusions limited to generative output published without human review, or to specified high-risk uses, or carve-backs preserving cover where a documented human approval step existed. The worst outcome is an absolute exclusion added quietly at renewal on a policy you did not re-read.

What does an affirmative AI endorsement actually buy?

It grants back, in named terms, the exposures the base wording leaves ambiguous: liability for erroneous or fabricated outputs, intellectual property claims arising from generated material, regulatory defence costs for algorithmic decision-making, and sometimes performance failure of a model you licensed. Expect it to come with conditions rather than as a free grant — disclosed use cases, a human-review requirement for certain outputs, restrictions on fully autonomous action, and a sublimit well below your main policy limit. The sublimit is the number that matters. An affirmative grant at ten per cent of your aggregate is a gesture, not a transfer.

If our AI vendor indemnifies us, do we still need coverage?

Yes, because a vendor indemnity and an insurance policy fail in different ways. Most model-provider indemnities are limited to third-party intellectual property claims about the output, conditioned on your using the safety features as configured and not modifying prompts or filters in specified ways, and capped at fees paid. They do not cover your customer suing you for a wrong answer, a regulator investigating your decision process, or your own defence costs while the vendor decides whether the conditions were met. Treat the indemnity as a narrow recovery route and the policy as the thing that pays your lawyers in month one.

What should we prepare before an insurance renewal that asks about AI?

An inventory of AI systems in use with the decision each one influences, whether a human reviews the output before it reaches a customer, what training or input data flows into each, and any governance artefacts you already hold — a risk assessment, an evaluation record, an incident log. Underwriters are asking these questions on supplemental applications now, and the answers become warranties. That is the underrated risk: an inaccurate application answer about human oversight can be grounds for rescission later, which is a worse position than an exclusion you knew about. Answer narrowly, answer accurately, and keep the version you submitted.

Does Your Cyber or Tech E&O Policy Cover an AI Failure? 2026 Coverage Guide

Why the Existing Wordings Do Not Fit

Every major line of business insurance was drafted around a mental model of how loss happens. Cyber assumes an intruder. Technology errors and omissions assumes a professional who performed a service below the standard of care. Media liability assumes a publisher who published something actionable. Each has a trigger, and the trigger is what an adjuster reads first.

An AI failure frequently satisfies none of them cleanly. Nobody broke in. The service performed exactly as designed — it generated a plausible answer, which is its whole function. Whether anything was published depends on where the output went. The result is not that you have no cover; it is that you have an argument, and arguments are expensive at precisely the moment you can least afford one.

The Claim Shapes, and Where Each One Usually Lands

TECH E&O

A customer relied on an output your product generated and suffered financial loss. This is the closest fit to a classic professional services claim — and the place an AI exclusion, if you have one, does the most damage.

MEDIA

Generated copy infringed a copyright or trade mark, or defamed someone. Media liability sections are where IP and reputational claims live, and many tech E&O towers include one — check whether generated content is carved out of it.

CYBER

The model surfaced another customer's data, or prompt logs retained sensitive input in a system that was later compromised. Here a cyber trigger genuinely attaches, but usually only to the data-compromise portion of the loss.

SEAM

A regulator opens an inquiry into an automated decision — hiring, pricing, eligibility. Defence costs for regulatory proceedings are often sublimited, sometimes only available under a specific endorsement, and rarely where you first look for them.

SEAM

Your agent took an action autonomously — sent an email, issued a refund, changed a record, placed an order. Consequential financial loss from an action rather than an opinion is the least well covered category in the market today.

UNDERRATED

Contractual liability. You promised a customer accuracy, uptime or indemnity in an MSA. Most policies exclude liability assumed under contract beyond what you would owe at law, so the promise your sales team made is uninsured by default.

The exclusion arrives at renewal, not at claim time. AI exclusions are being added to schedules as standard endorsements across cyber, tech E&O and professional lines. They do not require your consent beyond accepting the renewal terms, and they are easy to miss in a schedule of thirty endorsements. The single highest-value hour in this whole exercise is diffing this year's endorsement list against last year's before you bind.

Reading the Endorsement You Were Just Sent

Start with the definition. An exclusion is only as broad as its definition of artificial intelligence, and those definitions vary enormously — some are confined to generative systems producing content, others reach any system that makes or supports a decision using statistical inference, which on a literal reading captures a spam filter and a recommendation widget. If the definition is broad and the operative clause says arising out of, assume the exclusion reaches most of your product.

Then look for carve-backs. The negotiable middle ground is an exclusion that does not apply where a documented human review preceded the output reaching a third party, or where the use case appears on a disclosed schedule. That is a workable trade: you get cover for the systems you have governed, and you accept the exposure on the ones you have not. It also gives your governance programme a price tag your finance team can read, which is usually the argument that gets it funded.

What to Do Before Your Next Renewal

Diff the endorsement schedule. This year against last year, every policy. New AI exclusions are the most common silent change on the schedule right now.
Read the definition before the exclusion. The operative clause is meaningless without knowing what the policy calls AI. Broad definition plus "arising out of" equals a much bigger hole than it appears.
Inventory the systems and the decisions. Not a list of tools — a list of decisions each tool influences and who reviews the output. This is the document underwriters ask for and the one that supports a carve-back.
Price the affirmative endorsement and its sublimit. Ask specifically what the sublimit is, whether defence costs erode it, and whether regulatory proceedings are inside or outside it.
Reconcile your MSAs against the policy. Any accuracy warranty or uncapped indemnity you have signed is probably outside cover. Either change the contract template or accept the retained risk explicitly.
Read your vendor's indemnity for its conditions. Most are conditioned on unmodified safety settings and limited to IP claims. Confirm you are actually operating inside the conditions you are relying on.
Answer the supplemental application carefully. Narrowly and accurately, and keep the submitted copy. An overstated oversight control is a rescission argument later.

Frequently Asked Questions

We are a small SaaS company. Is this worth the effort at our size?

The effort scales down but the exposure does not, because the loss is driven by your customers' reliance rather than your headcount. A ten-person company whose product produces a number a customer acts on has the same claim shape as a large one, with less runway to fund a defence. The proportionate version for a small team is three things rather than a programme: diff your endorsements at renewal, know whether your tech E&O has an AI exclusion, and make sure your MSA does not promise accuracy your policy will not stand behind. That is an afternoon, once a year.

Does general liability help if an AI decision caused physical harm?

Sometimes, and this is the one place the older wordings work in your favour, because general liability responds to bodily injury and property damage regardless of how the negligence arose. The complications are the professional services exclusion, which pushes advisory failures back out to E&O, and the newer AI exclusions now appearing on general liability schedules too. If your system touches anything physical — logistics, clinical support, building systems, vehicle routing — this is the line to have specifically reviewed rather than assumed.

Is standalone AI liability insurance available yet?

Products marketed as AI-specific cover exist, largely as endorsements to existing towers rather than as a mature standalone market. Read them the way you would read any new product: what is the trigger, what is the sublimit, do defence costs erode the limit, what conditions precedent apply, and what does the exclusions list still remove. A grant-back of an exposure your base policy never clearly excluded is worth less than it sounds, and a small sublimit on a novel trigger is often worse value than negotiating the exclusion narrower on the policy you already hold.

Our model comes from a major provider. Does that reduce our exposure?

It changes who you can pursue afterwards, not who your customer sues first. You deployed the system, you chose the use case, and you are the counterparty on the contract, so the claim lands on you regardless of whose weights produced the output. The provider relationship matters at the recovery stage, and only to the extent the indemnity's conditions were met — which typically means you did not modify the safety configuration, you used the documented interfaces, and the claim is an IP claim rather than a reliance claim. Verify that before you treat it as risk transfer.

What single artefact most improves how we are underwritten?

A short, honest register of AI use cases with the decision each supports, the human-review step, and the date it was last reviewed. It is unglamorous and it does more work than anything else, because it lets an underwriter price a specific exposure instead of a category. It also supports the carve-back you want in the exclusion, gives your broker something concrete to market, and is the first document a regulator or a plaintiff will ask for. If you build only one governance artefact this year, build that one and keep it current.

The Policy Is Decided Before the Claim

Nobody negotiates an exclusion after a loss. The whole of this exercise happens in the four weeks before a renewal, and it consists of reading a schedule, knowing which of your systems make decisions customers rely on, and asking for a carve-back you can evidence.

Start with the inventory. It is the input to the underwriting conversation, the support for the carve-back, and the document you would otherwise have to assemble under deadline while a claim is open.