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Before approaching an insurer, build a documented picture of how each AI use could cause harm, how likely and serious each scenario may be, and what controls are already in place. A practical starting point is the UK Department for Science, Innovation and Technology’s 2026 method: score likelihood and impact separately from 1 to 5, then multiply them to prioritize attention. That score helps organize a conversation; it does not predict a premium, establish eligibility, or guarantee that a policy will cover a loss.
The useful outcome is a risk register you can explain: systems and uses, plausible loss scenarios, evidence and assumptions, current controls, planned treatments, and questions about actual policy wording. Insurers assess risk when deciding whether to offer coverage and on what terms, but the applicable answer depends on the insurer, jurisdiction, and policy documents.
What does an AI risk assessment do before an insurance purchase?
It turns broad concerns about artificial intelligence into specific, reviewable scenarios tied to your organization’s actual systems and decisions. That gives you a sound basis to prioritize risk reduction and ask a broker or insurer how the organization’s exposures fit the coverage being considered.
An AI risk assessment and an insurance decision are related, but they are not the same thing. A risk score is an internal prioritization tool. Underwriting is the insurer’s process for assessing risk and deciding whether to offer a policy, what it costs, and/or how much coverage to provide, as the National Association of Insurance Commissioners (NAIC) explains. A score produced by your organization cannot determine those outcomes.
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The UK Department for Science, Innovation and Technology’s AI Risk Management Toolkit: guidance, published 8 September 2026, supports risk management across design, operation, procurement, and delivery. It organizes the work around identifying and assessing risks, treating them, monitoring them, and reporting them. The ISO/IEC 23894:2023 standard is also guidance for organizations that develop, produce, deploy, or use AI products and services. It is an optional risk-management reference, not an insurance product or a stated prerequisite for buying coverage.
Neither source establishes standard AI-specific insurance policies, exclusions, limits, prices, or application requirements. Treat your assessment as preparation for a jurisdiction- and policy-specific discussion, not proof that a particular loss will be insured.
How do I scope the AI use I need to assess?
Start with an inventory of each AI system or service in use or planned, including tools bought from a vendor. Assess the real use, not just a model name or a vendor’s general description: what the system does, whose information or interests it affects, and what people do with its output.
Record the decision context
For each use, write down the purpose, the process it supports, whether it affects internal operations or customers, and the people who may rely on or be affected by its output. Record who is accountable for the use and whether the system recommends an action, produces content, or directly supports a decision. If people can override or review its output, describe how that works in practice.
Include purchased and changing systems
Include vendor-provided tools, integrations, and planned deployments, even if your organization did not build the underlying model. Procurement is expressly within the UK toolkit’s scope. Note the relevant vendor and the function you depend on so that a later conversation can identify where your organization’s controls end and a third party’s service begins. Do not assume that using a vendor removes your organization’s exposure.
Keep the inventory tied to actual use. A general-purpose system may support several distinct activities, and those activities can create different affected parties and consequences. Record them separately when the decision, data, operational dependence, or plausible harm differs materially.
What AI risks should a business document for an insurer?
Document plausible ways the specific use could result in harm, connecting a cause to an affected party and a consequence. Avoid generic entries such as “AI failure”; a useful scenario explains what might go wrong and what the organization or another party could experience.
Build scenarios around the use case
- Incorrect output: an inaccurate response or recommendation is relied on and contributes to a poor business or customer outcome. The NAIC cautions that generated content can sound accurate while being incorrect and says important AI-generated information should be reviewed carefully.
- Unfair or biased decisions: an AI-supported process produces an adverse result for a person or group, or a decision cannot be adequately explained or challenged.
- Privacy or data issues: information is exposed, used in an unintended way, or handled in a way that creates harm for people or the organization.
- Security or misuse: a system or its output is used in a harmful or unauthorized way.
- Service interruption: an AI service, a vendor, or a dependent process is unavailable and disrupts operations.
- Human over-reliance: staff accept output without the review or judgment that the use requires.
- Vendor failure: a supplier change, failure, or limitation affects a process on which the organization depends.
- Reputational damage: a harmful outcome affects trust in the organization, its service, or its decisions.
These are prompts, not a universal checklist. Keep only scenarios that fit the use, and add others that emerge from the organization’s operations. For each, identify the initiating event or condition, who or what is exposed, the plausible consequence, and any assumptions that connect them. This makes the risk understandable to decision-makers and easier to discuss with a broker.
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Separate distinct consequences
A single event can produce more than one kind of impact. For example, a system error could create operational downtime as well as complaints or financial loss. The toolkit describes weighting possible outcomes or using the highest impact score when a scenario has several consequences. Choose one approach, explain why it fits, and use it consistently; do not hide a severe consequence by averaging it with less serious ones.
How do you quantify AI risk without implying false precision?
Use a transparent scoring method to compare scenarios, and document the evidence and judgment behind each score. The toolkit recommends scoring likelihood and impact on separate 1-to-5 scales and multiplying the two scores; it also allows an organization to weight results when it has a clear risk-appetite rationale.
Define what each score means for your organization
Before scoring, define the meaning of each point on both scales in terms people in your organization can apply. Likelihood should reflect the chance of the described scenario within a stated context or time horizon; impact should reflect the seriousness of the consequences if it occurs. The toolkit supplies the 1-to-5 ranges and multiplication method, but your organization needs to make its own scale definitions understandable and consistent.
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Do not use an unexplained number. Record the time horizon, scope, and reasoning behind the estimate. If one team calls a likelihood score of 3 “possible” and another uses 3 for a materially different level of exposure, the resulting scores cannot support reliable prioritization.
Apply the multiplication consistently
For an initial score, multiply the likelihood value by the impact value. For example, a hypothetical scenario scored at likelihood 3 and impact 4 would produce an initial score of 12. Those inputs are illustrative only: an organization should not copy them without evidence about its own system and exposure. The result is a prioritization aid, not a statistical estimate of expected loss or an insurer’s rating.
If you weight results because some consequences matter more under your risk appetite, document the reason and the method. Weighting should make the organization’s priorities clearer, not make a preferred result appear more precise or favorable.
Use evidence where it exists and judgment where it does not
Likelihood estimates may draw on system-specific metrics, historical errors or incidents, testing, exposure, expert judgment, and safe experimentation or monitoring. For impact, use measurable consequences where data supports them, such as financial loss, operational downtime, or complaint volumes. The toolkit provides a structured assessment method, not a published study of AI loss frequency.
Some consequences, including effects on trust, morale, or strategy, may not translate cleanly into a financial estimate. In those cases, consult relevant stakeholders and subject-matter experts and explain the reasoning rather than assigning a seemingly exact monetary value. Distinguish observed information from assumptions and expert judgment so that a later reviewer can tell how the estimate was formed.
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Keep the score in its proper role
Use the score to decide which scenarios need management attention, further evidence, or stronger treatment. It is not an actuarial forecast, a premium quote, an insurability decision, or a promise about a claim. Underwriting and coverage decisions depend on the insurer’s assessment and the policy terms that apply to the buyer.
How should I record controls and decide what to do about each risk?
For every scenario, distinguish controls already operating from actions that are merely planned. Then record how the organization will treat the remaining risk, who owns the action, when it is due, and how completion or effectiveness will be checked.
Describe controls in operational terms
Record what actually happens in the workflow, not just a policy statement. For example, if important AI-generated information is reviewed, specify who reviews it, when review occurs, and what happens when the output appears wrong. If a control is not yet operating or its effectiveness has not been checked, say so rather than scoring it as a proven safeguard.
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Connect controls to the scenario they are meant to address. A control may reduce the chance of an error, limit the consequences if one occurs, or improve detection and response. Explain which effect you expect and what evidence supports that expectation, such as test results, incident records, or monitoring data.
Choose a treatment and assign accountability
The UK toolkit treats risk treatment as part of the ongoing assessment process. Depending on the organization’s process and risk appetite, a treatment decision may be to reduce, avoid, transfer, or accept a risk. Record the decision and its rationale. For a risk-reduction action, name an owner, target date, and verification method so that the treatment can be tracked rather than left as a general intention.
Insurance is one possible form of risk transfer, not a substitute for technical, operational, or governance controls. A policy cannot be assumed to prevent an error, keep a service running, or compensate for every consequence. Continue to manage the underlying exposure whether or not coverage is available.
When should an AI risk assessment be updated?
Make the assessment a continuing process, with a review cadence and change triggers suited to the system and its consequences. The UK toolkit treats monitoring and reporting as ongoing parts of risk management, rather than a one-time exercise completed before a purchase.
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Revisit a scenario when something material changes: the model or data, the vendor, the scale of use, the user population, or the business purpose. Also set a routine review cadence appropriate to the organization’s use and risk. The evidence does not establish one interval that fits every AI deployment, so make the chosen schedule explicit and explain who is responsible for following it.
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Track signals that can improve the next assessment
Monitor incidents, errors, downtime, complaints, and the system-specific performance measures relevant to the use. Record what was observed and how it compares with the assumptions behind likelihood and impact estimates. A review should update the scenario, scores, controls, or treatment where the evidence warrants it, rather than simply carrying forward last time’s numbers.
The Federal Reserve’s Framework for the Supervision of Insurance Organizations highlights model inventory, validation, continuous performance monitoring, and governance as model-risk practices for insurance organizations it supervises. That is useful context for understanding insurer governance, not a universal buyer rule or a required schedule for every business.
What should I bring to a broker or insurer?
Bring enough information to explain the use, the plausible harms, what you know about them, and how the organization manages them. A concise, internally consistent record is more useful than a high score without context.
- An inventory of AI systems and the business activities they support, including purchased services.
- Prioritized scenarios that identify causes, affected parties, and consequences.
- Likelihood and impact scores, the organization’s scale definitions, and the evidence or assumptions behind each estimate.
- Controls already in operation, treatment decisions, accountable owners, and open actions.
- Relevant incident, error, downtime, complaint, testing, or monitoring records.
- Vendor information and unresolved questions about dependencies or exposures.
Be candid about uncertainty and open issues. If an estimate depends on limited incident history or expert judgment rather than strong system-specific data, state that plainly. The goal is a useful underwriting conversation, not a presentation that makes every exposure appear resolved.
What should I ask about the actual policy wording?
Ask the broker or insurer to address the documented scenarios against the policy documents and the business’s jurisdiction and use case. Do not infer that a risk is covered because a policy uses broad language about technology, or that a particular exclusion applies without checking the wording and relevant context.
- Which legal entities, people, and business activities are insured, and how are the AI uses described?
- Which types of loss relevant to the scenarios are covered, and what exclusions or carve-backs may affect them?
- What limits and sublimits apply, and what deductible or retention would the organization bear?
- How do claims reporting and defense arrangements work, and are claims-made or occurrence triggers relevant?
- Where relevant, what retroactive date or continuity terms apply?
- How are vendors, subcontractors, and third-party services treated?
- What geographic scope applies, and how does the wording address AI-generated or AI-supported decisions?
- What reporting duties or other policy conditions could affect a claim?
These are questions to verify, not standard answers established across the market. Compare the actual offers and policy wording against the risks you documented. If the explanation is unclear, ask for clarification from the licensed broker or insurer before relying on an assumption about coverage.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do insurance regulators’ AI rules mean for a buyer?
Regulatory material about an insurer’s use of AI can help explain the insurer’s governance responsibilities, but it should not be mistaken for a universal application checklist or a promise about coverage. Scope matters: the cited sources address particular regulators, organizations, and activities.
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New York Department of Financial Services Circular Letter No. 7 concerns insurers authorized in New York and AI systems and external consumer data in underwriting and pricing. Its guidance addresses risk-appropriate governance, actuarial support, regular testing, documentation, and third-party vendor controls. It describes expectations for insurers, not a general buyer application form.
The Texas Department of Insurance Commissioner’s Bulletin B-0003-26, dated 12 June 2026, addresses regulated entities and their agents or representatives and reiterates that AI-supported decisions affecting consumers remain subject to applicable insurance laws. Its scope is Texas-regulated activity; it does not specify policy terms available to every business.
Similarly, the Federal Reserve framework applies to supervised insurance organizations. Use it as context for insurer model risk rather than as a rule that automatically governs a buyer’s assessment. For the organization seeking coverage, the practical next step remains to provide accurate information and verify how the particular policy treats its activities and scenarios.
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A risk register helps only if its entries are specific, traceable, and connected to decisions. Watch for these common weaknesses:
- Scoring a technology instead of a use: “the AI is risky” does not say which activity, affected party, or harm is at issue.
- Using a number without a scale: likelihood and impact values are hard to interpret when teams have not defined what each point means.
- Presenting judgment as measurement: label assumptions and expert views; do not imply that a score comes from a loss-frequency study when it does not.
- Ignoring non-financial effects: where trust, morale, or strategic consequences matter, record stakeholder judgment instead of forcing an unsupported financial value.
- Counting planned controls as working controls: separate actions still in progress from safeguards operating in the real workflow.
- Treating a vendor relationship as a complete transfer of responsibility: identify the service dependency and clarify vendor-related questions with the broker or insurer.
- Assuming an assessment establishes coverage: a risk register supports discussion; the policy wording and applicable jurisdiction govern the coverage question.
- Failing to revisit old estimates: a changed model, dataset, vendor, scale, population, or purpose can make an earlier score stale.
Keeping these distinctions visible makes the assessment more credible and more useful for management, procurement, and an insurance discussion.
Frequently Asked Questions
Is ISO/IEC 23894 required before applying for AI insurance?
The standard is guidance on AI risk management for organizations that develop, produce, deploy, or use AI products and services. The cited material does not establish it as an insurance application requirement.
Can an insurer use AI when underwriting or pricing my business?
Insurers may use AI, but they remain subject to applicable insurance laws and oversight. Requirements vary by jurisdiction; for example, New York guidance addresses authorized insurers’ use of AI and external consumer data in underwriting and pricing.
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No. Guidance about insurer governance does not substitute for the policy documents issued for your situation. Ask a licensed broker or insurer to explain relevant wording and requirements for your jurisdiction and use.
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