What AI insurance underwriters actually ask before writing a policy.

The underwriting problem that makes AI insurance different

Pricing conventional liability insurance depends on actuarial models built from historical claims data. A professional indemnity insurer pricing a law firm's E&O can draw on decades of data about the frequency and severity of legal malpractice claims, adjusted for firm size, practice area, and claims history. The risk is well-characterised.

AI agent liability has no equivalent data history. The systems being insured are novel, their failure modes are probabilistic rather than predictable, and the population of deployed systems is growing faster than the claims data that would allow actuarial modelling. This creates a fundamental underwriting challenge: the insurer cannot price from historical data alone and must rely instead on observable indicators of governance quality as a proxy for risk.

This is why every specialist AI liability underwriter focuses on documentation. Documentation is not bureaucracy. It is the closest available signal to what actuarial data would normally provide. An enterprise with strong governance documentation is, in the underwriter's assessment, a lower risk than an identical enterprise without it, because the documentation demonstrates structured oversight that reduces the probability of harmful failures and enables faster remediation when failures occur.

Understanding this logic reframes the documentation work. Building an AI governance record is not compliance overhead. It is directly improving your insurance eligibility and reducing your expected premium. The two activities are identical at the level of the evidence produced.

The four document categories every underwriter requires

Across Armilla, AIUC, the Lloyd's market, Munich Re aiSure, and Testudo, four categories of documentation are consistently required before a binding quotation is issued. These categories differ in label and format across carriers but are substantively the same.

1. Technical system description

The underwriter needs to know what the AI system does, at a level of specificity that allows them to assess the liability profile of its outputs. A description of "our AI assistant helps customers with queries" is not adequate. The required level of detail covers: the type of model used (large language model, classification model, recommendation engine, autonomous agent); whether the model is proprietary or a third-party foundation model accessed via API; the data on which the model operates in production; the categories of outputs it produces; and the channels through which those outputs reach end users or feed other systems.

The EU AI Act's technical documentation requirements under Article 11 and Annex IV provide a useful template for this description. An Annex IV technical documentation file completed for EU AI Act compliance purposes is, in most cases, sufficient for the technical description section of an AI liability underwriting questionnaire. Organisations that have not yet built their Annex IV documentation should treat the underwriting questionnaire as the reason to build it now. Further detail on Article 11 requirements is at agentliability.eu.

2. Governance and oversight record

The governance section of an underwriting questionnaire addresses who is responsible for the AI system and what controls they exercise over it. The questions typically cover: designated ownership at the organisational level (who is accountable when something goes wrong); the review and approval process for new AI deployments; the ongoing oversight mechanism (who reviews outputs, at what frequency, using what criteria); and the escalation path when an incident or anomaly occurs.

Underwriters are particularly interested in human oversight at decision points. For AI agents that take consequential actions, such as communicating with customers, generating documents, making recommendations, or executing transactions, the governance section should document whether there is a human approval requirement before the action is taken or whether the system acts autonomously. Fully autonomous consequential actions without human approval gates are a significant risk signal for most underwriters. Munich Re aiSure, which uses a parametric coverage model, prices autonomous system exposure differently from supervised deployment: the scope of autonomous action is directly related to coverage limits and premium.

3. Monitoring and incident record

The monitoring section demonstrates that the organisation is actively tracking the AI system's performance in production and responding to anomalies. Questions in this section cover: how the system's outputs are monitored; what metrics are tracked; what thresholds trigger review or escalation; and whether there is a formal incident log.

An incident log is not a negative signal for underwriters. An enterprise that has experienced AI-related near-misses or minor incidents and has documented them, assessed root causes, and implemented corrective actions is demonstrating exactly the kind of active governance that underwriters look for. An enterprise with no incident history and no monitoring record is a different kind of signal: it may reflect genuinely clean performance, or it may reflect absence of monitoring. Underwriters assume the latter until documentation suggests otherwise.

Post-market monitoring requirements under EU AI Act Article 72 require deployers of high-risk AI systems to maintain a post-market monitoring plan and record. This document, if maintained correctly, satisfies the monitoring section of an AI liability underwriting questionnaire.

4. Scope constraints and exclusion documentation

Underwriters want to know what the AI system cannot do and what safeguards prevent it from acting outside its intended scope. This section covers: documented constraints on the system's output range; human approval requirements before consequential actions; technical safeguards against prompt injection, data leakage, or scope drift; and any known limitations or failure modes that have been identified and accepted.

This documentation reflects the risk management system required under EU AI Act Article 9 for high-risk AI systems, which requires deployers to maintain a risk management system that identifies, analyses, and assesses risks and implements risk management measures. The Article 9 risk management documentation, if structured to include scope constraints and safeguards as required by the Act, covers the underwriter's scope constraints section.

Carrier-specific requirements: Armilla, AIUC, and Lloyd's

Armilla

Armilla, founded in Toronto and operating as a Lloyd's of London coverholder underwritten by Chaucer, raised $25 million in January 2026 and expanded its per-organisation coverage limits to $25 million. Armilla is the most relevant specialist carrier for European enterprise clients because it has explicitly extended its coverage to include EU AI Act regulatory violations.

Armilla's underwriting process begins with a governance questionnaire that the applicant completes directly or through a broker. The questionnaire covers the four documentation categories described above, with particular emphasis on the governance and oversight section. Armilla's Trustible partnership in the North American market demonstrated their model: Trustible provides a governance documentation platform whose output feeds Armilla's underwriting assessment directly. This reduces friction in the submission process and improves pricing accuracy. Armilla has indicated that its European expansion will follow the same principle, with governance documentation requirements specifically mapped to EU AI Act compliance requirements.

Armilla excludes medical diagnosis, mental health support, and legal advice from its core product. These exclusions reflect the significant liability exposure in those sectors and the absence of adequate governance standards that Armilla can use as underwriting evidence. Organisations in healthcare and legal services should explore sector-specific coverage routes.

AIUC and the AIUC-1 standard

The Artificial Intelligence Underwriting Company (AIUC), which emerged from stealth in July 2025 with $15 million in seed funding led by Nat Friedman, combines certification with coverage. Its AIUC-1 standard is the first published AI insurance certification standard, structured around six assessment dimensions: safety, reliability, alignment, interpretability, robustness, and governance. An enterprise that completes a full AIUC-1 audit and achieves a high score is eligible for coverage at a premium that reflects the assessed governance quality of the specific system.

The AIUC model is the most actuarially sophisticated approach in the market: by connecting audit scores to pricing, AIUC is building the dataset that will eventually allow the market to price AI liability from observable risk indicators rather than from governance proxies alone. ElevenLabs became the first AIUC-1-backed policyholder in February 2026, insuring its AI voice agents under a policy priced on the basis of an AIUC-1 audit outcome.

AIUC-1 was designed for the US legal and regulatory context. It does not map specifically to EU AI Act compliance obligations. European enterprises seeking AIUC coverage should supplement their AIUC-1 preparation with EU AI Act compliance documentation to ensure the governance record covers both frameworks. The Agent Certified framework at agentcertified.eu is structured around EU AI Act requirements and maps to the same dimensions that AIUC-1 assesses.

Lloyd's market endorsements

Lloyd's managing agents circulated draft AI endorsements for consultation with syndicates and brokers in early 2026. These endorsements extend existing professional liability and technology errors and omissions policies to cover AI-specific losses, including claims arising from model errors, hallucinations, harmful outputs, and AI agent failures. The endorsements are being developed in parallel with Lloyd's Emerging Risk Group guidance on AI underwriting, which establishes the framework within which managing agents can write AI liability.

The underwriting conditions in the circulating drafts are converging on three requirements. First, documented human oversight of consequential AI decisions: policies will include a condition precedent requiring that the insured maintains documented human review processes for AI-assisted decisions above a defined threshold of significance. Second, a post-incident remediation record: the insured must demonstrate that any material AI incidents have been investigated, root-caused, and corrected, with that process documented. Third, training data provenance: for AI systems built on foundation models, the insured must be able to describe the model's training data governance at a level that allows the underwriter to assess the exposure to data-related claims (intellectual property, privacy, bias).

Brokers placing AI liability into the Lloyd's market will increasingly need to provide these three documents as part of the presentation. Enterprises that have built this documentation before approaching the market will have a significantly faster path to a binding indication than those who are assembling it in response to a carrier's request.

What disqualifies an AI deployment from coverage

Several characteristics consistently produce a decline from specialist AI underwriters or result in coverage terms that are too narrow or expensive to be useful.

No documentation whatsoever. An enterprise that cannot produce any governance, monitoring, or technical documentation for its AI system is presenting an unquantifiable risk. No specialist underwriter will write it at a viable premium. This is the most common single reason for an unsuccessful coverage approach in the current market.

Deployment in excluded categories. Most specialist AI carriers explicitly exclude medical diagnosis, mental health support, legal advice, and autonomous weapon systems. For enterprises in these sectors, coverage must be sought through sector-specific routes, typically a specialist professional liability or medical malpractice insurer that is developing AI-specific language, or through a Lloyd's syndicate focused on medical or legal professional liability.

Prior incidents with no documented remediation. An enterprise that has experienced AI-related harm events and cannot show that those events were investigated, root-caused, and corrected is presenting a high-risk signal. The incidents themselves are manageable if properly handled. The absence of remediation documentation is not.

Opaque third-party model supply chains. AIUC in particular requires that the operator can describe the training data governance of the model it is deploying. For enterprises using third-party foundation models via API from providers that do not publish training data information, this creates underwriting difficulty. The Agent Certified dimension on training data governance, drawn from EU AI Act Article 10, addresses this: it guides enterprises through the documentation they can and should obtain from foundation model providers even when full transparency is not available.

Full autonomy without human approval gates for consequential actions. A fully autonomous AI agent that can commit the enterprise to contracts, make payments, or send consequential communications without human approval before the action is taken represents a tail risk that underwriters are reluctant to write at standard terms. Enterprises should document the scope of autonomous action clearly and implement approval gates for actions above defined thresholds. This documentation both improves insurability and satisfies the human oversight requirements of EU AI Act Article 14.

Preparing a strong underwriting submission

The most effective approach to AI insurance preparation treats the underwriting submission as the compliance documentation, assembled in advance rather than in response to a carrier's questionnaire under deadline pressure.

Concretely, this means four workstreams running in parallel. First, complete the technical documentation for each AI system in scope: what it does, what model it uses, what data it processes, what outputs it produces. Second, document the governance structure: ownership, oversight process, approval gates for consequential actions, incident escalation paths. Third, establish and maintain a monitoring log: performance metrics, anomaly records, and incident documentation. Fourth, document scope constraints: what the system cannot do, what safeguards prevent scope drift, what human approval is required before irreversible actions.

For organisations building EU AI Act compliance programmes under the August 2026 deadline, this workstream is the same as the compliance workstream. The documentation produced for Articles 9 (risk management), 11 (technical documentation), 12 (logging and record-keeping), 13 (transparency towards deployers), 14 (human oversight), and 72 (post-market monitoring) is the documentation an underwriter will ask for. The return on compliance investment is therefore doubled: it satisfies the regulatory requirement and positions the enterprise for coverage when products become available.

Start the preparation at least 90 days before the intended coverage start date. Armilla's technical assessment takes two to four weeks. AIUC's full audit takes four to eight weeks. If documentation is incomplete at the start of that process, the timeline extends accordingly. Organisations that approach the market without documentation will not obtain coverage within a normal renewal cycle.

For the structured seven-dimension certification framework that produces this documentation systematically, see the Agent Certified methodology. For a full analysis of EU AI Act deployer documentation requirements, see the Article 26 guide on agentliability.eu.

Frequently asked questions