What Is AI Insurance?

AI insurance is cover for financial loss caused by an AI system. That includes a bad output, an outright failure, or the liabilities that follow when an AI acts on someone’s behalf and something goes wrong. It is the risk-transfer counterpart to AI assurance: assurance works to prevent and detect failures, while insurance pays for the ones that happen anyway. As covered in the introduction, most organizations adopting AI at scale end up wanting both.

The complication is that almost no existing line of insurance was designed for autonomous AI. So “AI insurance” today is really two things at once: the awkward way traditional policies respond when AI is involved, and a small, fast-growing set of products built specifically for AI risk. This page covers both, plus the gap between them.

How AI breaks the existing lines

Every major line of commercial cover was written for human mistakes or conventional software, and each responds uneasily to AI.

• Professional indemnity and technology errors and omissions. These respond to negligent acts in providing professional services. They cannot cleanly answer whether operating an AI agent counts as providing a professional service, or whether a hallucinated output is a negligent act, a product defect, or neither.
• Directors and officers. A new class of “failure to govern AI” claims is emerging, and securities regulators have signaled that AI can be a material disclosure issue. Some carriers are adding AI-specific carve-outs at renewal.
• Cyber. Most cyber wordings are silent on AI misbehavior. This “silent AI” exposure is openly discussed in the reinsurance market, and newer wordings increasingly add explicit AI carve-outs.
• Product liability and general liability. Jurisdictionally messy. The EU had proposed an AI Liability Directive to ease the burden of proving causation for high-risk AI, but the Commission withdrew that proposal in October 2025, leaving the question unsettled at EU level.
• Trade credit and surety. Less obvious, but emerging as agents begin to initiate financial transactions autonomously.

The thread running through all of these is silent AI: exposure hidden inside policies that were never priced or worded for it. Faced with that uncertainty, many insurers are doing the cautious thing and adding explicit AI exclusions, which protects the insurer but widens the gap in cover for the buyer.

The new AI-specific products

Into that gap, a distinct set of products has appeared. They are still narrower than the full risk, but they are real and the pace is picking up.

• Munich Re aiSure. The longest-running example, offered since 2018. It is a performance guarantee that pays out when a model’s measured performance falls below an agreed threshold, a parametric approach rather than traditional indemnity. In February 2026, Munich Re extended aiSure to AI vendors through a partnership with Mosaic Insurance, with up to USD 15 million in capacity, explicitly covering generative-AI errors and hallucinations.
• Armilla. A managing general agent and the first Lloyd’s coverholder dedicated to AI liability. Its standalone AI liability policy, launched in 2025, was expanded in January 2026 to limits of up to USD 25 million per organization. Armilla also launched “Vanguard AI” with Chaucer, a coordinated structure that combines cyber, technology errors and omissions, and standalone AI liability in a single placement to remove gaps and overlaps between them.
• AIUC and ElevenLabs. The clearest public example of embedded AI cover. In February 2026, ElevenLabs went live with insurance for its AI agents enabled by certification to AIUC’s AIUC-1 standard, covering failures such as hallucinations, prompt injection, unauthorized actions, and incorrect advice. This is the pattern where assurance and insurance fuse: the certificate is what unlocks the cover.
• Vouch. A standalone AI insurance product aimed at startups and AI companies, covering areas such as AI errors and omissions, bias and discrimination, hallucinations, intellectual property infringement, and regulatory exposure.
• Hyperscaler-led programs. A distinct model in which a cloud provider arranges affirmative AI cover for failures of its own AI services, with capacity from established carriers. These programs point to a future where cover travels with the platform rather than being bought separately.

The shape of the gap

Two things stand out about the market as it exists today.

First, the most valuable product is still largely missing: cover that wraps assurance evidence directly, and that prices on the continuous data about how a system behaves rather than on an annual proposal form. The building blocks exist, the AIUC-1 and aiSure models point at it, but a mature, widely available version does not yet.

Second, and revealingly, demand right now is driven less by a single famous AI loss than by carriers retreating. As insurers add AI exclusions to existing policies, buyers discover gaps in cover they assumed they had, and go looking for something to fill them. The gap, in other words, is being created from the supply side as much as the demand side. That is a familiar pattern in emerging insurance categories, and it usually precedes a faster build-out once a high-profile loss or a regulatory trigger arrives.

Who underwrites and who distributes

AI insurance runs through the same machinery as any specialty line. Primary carriers and managing general agents write the cover; Lloyd’s syndicates and coverholders provide a structured route to capacity; reinsurers stand behind the carriers and increasingly shape the terms through treaty language on AI and accumulation; and brokers place the risk. What is new is how often assurance providers sit alongside this chain, supplying the evidence that makes a risk underwritable in the first place. The Landscape maps these players by category and by region.

Where this connects

AI insurance is one layer of a larger picture. The risks it prices are organized in the AI risk stack, and what an insurer will cover and at what price increasingly depends on the assurance evidence described in What Is AI Assurance? and on the standards and regulations that set the baseline. Those companion pillars go deeper on each.

Common questions

What is AI insurance? AI insurance is cover for financial loss caused by an AI system, whether from a bad output, a failure, or the liabilities that follow. It includes both AI-specific products and the way existing lines such as professional indemnity, cyber, and directors and officers respond, or fail to respond, when AI is involved.

Does my existing business insurance cover AI? Often not cleanly. Most traditional policies were written for human error or conventional software and respond awkwardly to AI failures. Many insurers have added explicit AI exclusions to cyber and other lines, so cover that buyers assume exists may have been carved out. Check the wordings rather than assume.

What AI-specific insurance products exist today? A small but growing set. Munich Re’s aiSure has offered AI performance guarantees since 2018. In 2026, AIUC-1-backed cover went live with ElevenLabs, and managing general agents such as Armilla write standalone AI liability policies through Lloyd’s. Vouch offers AI cover aimed at startups, and hyperscaler-led programs are emerging. Available cover is still narrower than the risk.

What is the silent AI problem? Silent AI is AI-related exposure hidden inside policies that were never priced or worded for it. A cyber or professional indemnity policy may neither clearly cover nor clearly exclude an AI failure, leaving insurers with unintended exposure and buyers with uncertain protection. Insurers are responding by adding explicit AI language, often exclusions.