The Tool Desk
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Before approaching insurers, document how each AI system is used, the plausible losses it could cause, how likely and severe each scenario appears, and which controls are already in place. A transparent likelihood-by-impact score can help prioritize that work, but it is not an insurance quote, an actuarial forecast, or a guarantee of coverage. Use the assessment to have a more specific conversation with a licensed broker or insurer about your actual use case and policy wording.
Why assess AI risk before asking for insurance?
An AI risk assessment gives your organization a structured account of what could go wrong, who could be affected, how serious the consequences might be, and what you are doing about them. That is more useful in an insurance discussion than a general statement that the business “uses AI”: it connects the technology to particular activities, exposures, and controls.
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The assessment is also a management tool, not merely an insurance document. The UK Department for Science, Innovation and Technology’s AI Risk Management Toolkit, published 8 September 2026, treats risk work as a continuing cycle: identify and assess risks, treat them, monitor them, and report them across design, operation, procurement, and delivery. It states: “A clear understanding and justification of AI risks and treatment strategies is key to using AI responsibly and ethically.”
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How do I scope the AI use I need to assess?
Start with an inventory of systems in use or planned, including tools bought from vendors and AI features embedded in other services. Assess each use case separately when its purpose, users, affected people, decisions, or potential consequences differ; a single organization-wide score can conceal those differences.
Record the use case, not just the product name
For each entry, capture:
- System and provider: the product or service, vendor, and relevant model or feature if known.
- Purpose and status: what the AI does and whether it is proposed, piloted, or in routine use.
- Where it operates: internal work, customer-facing service, or both.
- Decision or action supported: what a person or process may do in response to its output.
- People affected: who relies on, receives, or may be affected by the output.
- Accountability: the person responsible for the use and the team that operates it.
- Dependencies: relevant data, vendors, integrations, and human review points.
Include procurement and vendor-provided services, not only systems your organization built itself. The UK toolkit explicitly covers procurement as well as other lifecycle stages. ISO/IEC 23894:2023 is broader guidance for organizations developing, producing, deploying, or using AI products and services; its official ISO listing describes the standard and its publication formats. Neither source says that a standard or certification is required to seek insurance.
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What AI risks should a business document for an insurer?
Write scenarios as cause, affected party, and consequence. “The model may be biased” is too broad to assess; a scenario should explain which use could produce an unfair outcome, who might experience it, and what could follow.
Depending on the use case, consider scenarios involving:
- Incorrect output: inaccurate generated content or a flawed recommendation leads a staff member or customer to take a harmful action. The NAIC notes that generative AI content can sound accurate while being incorrect, and emphasizes careful review of important AI-generated information.
- Unfair or biased decisions: an AI-supported decision treats people inequitably or produces a harmful result for a particular group.
- Privacy or data issues: sensitive or inappropriate information is exposed, mishandled, or used in a way that creates harm.
- Security, misuse, or failure: a system is misused, compromised, or unavailable when the business depends on it.
- Human over-reliance: a user accepts an output without suitable review, or the review process fails to catch an error.
- Vendor or dependency failure: a third-party service changes, fails, or becomes unavailable and disrupts the use case.
- Operational, financial, or reputational harm: a failure leads to downtime, financial loss, complaints, loss of trust, or another consequence relevant to the organization.
These are prompts, not a universal checklist of risks that every AI use presents. Include only plausible scenarios for the system and its context; add other risks when the use case warrants them. A useful scenario statement identifies a trigger or failure, the people or operations exposed, and the resulting harm. Separate scenarios if they have meaningfully different consequences, controls, or likelihoods.
How do you quantify AI risk?
Score likelihood and impact separately on a 1-to-5 scale, then multiply them for an initial prioritization score. The UK toolkit recommends this method and allows organizations to weight results when they have a clear rationale grounded in relative importance and risk appetite; it is a structured judgment method, not a statistical loss estimate.
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Define what each score means locally
Before scoring, write down what a 1, 2, 3, 4, and 5 mean for your organization. The reviewed toolkit provides the scale but does not establish universal descriptions that fit every business, so define and apply your own consistently. For likelihood, explain the time horizon and what evidence would make an event more or less plausible. For impact, specify the kinds of consequences being considered and whose perspective matters.
Keep the two judgments distinct. A scenario can be unlikely but severe, or relatively likely but limited in consequence. Record the likelihood score, impact score, multiplication, and the reasoning behind each input rather than keeping only the final number. Do not create universal labels such as “low,” “medium,” or “high” for score ranges unless your organization has adopted and documented those thresholds.
Use evidence without creating false precision
Likelihood estimates may draw on prior incidents, system-specific performance measures, exposure, testing, expert judgment, or evidence from a safe pilot. The toolkit encourages relevant metrics and experimentation where appropriate, but does not provide a published loss-frequency study for AI risks. A score should therefore make clear whether it is supported by observed evidence, a test, informed judgment, or an assumption that needs validation.
Where data supports it, estimate impacts through measurable outcomes such as financial loss, operational downtime, or complaint volumes. Some consequences—such as loss of trust, morale, or strategic harm—may not translate cleanly into a precise monetary amount. In those cases, consult relevant stakeholders and experts, explain the judgment, and avoid presenting an invented dollar figure as fact.
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Illustrative calculation
Suppose a business depends on an AI-enabled service for a customer-facing workflow. A scenario might be: “A service interruption prevents staff from completing the workflow, causing delays and customer complaints.” If the organization’s documented definitions lead its team to assign likelihood 3 and impact 4, the initial score is 12. Those numbers are an illustration of the arithmetic, not an established rating for this scenario, and the toolkit does not make 12 a universal risk category.
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Use weighting only when the organization can explain why one risk dimension or consequence deserves more emphasis under its stated risk appetite. Keep the unweighted inputs visible as well, so a weighted total does not hide whether concern comes from likelihood, impact, or both.
What should the risk register contain?
A compact register makes the assessment reviewable and actionable. Keep one row per distinct scenario and retain enough context to reproduce the judgment later.
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|---|---|
| Use case | System, purpose, operating context, affected people, and accountable owner. |
| Scenario | Cause or failure, exposed party or operation, and potential consequence. |
| Likelihood and impact | Separate 1–5 scores, the scale definitions used, and the reasoning for each. |
| Evidence and assumptions | Incidents, metrics, testing, expert judgment, pilot observations, and important uncertainties. |
| Initial score | Likelihood multiplied by impact; include any weighting separately with its rationale. |
| Controls | Controls already operating, who owns them, and how they are checked. |
| Treatment | Reduce, avoid, transfer, or accept the risk, with the decision owner and rationale. |
| Actions and review | Open action, owner, target date, verification method, review date, and update trigger. |
This is a practical format, not a prescribed insurance application form. A register that records only a score is difficult to use: it does not show what the number represents, what evidence supports it, or whether the organization has changed the underlying use.
How should we treat and monitor the risks?
For each scenario, document controls already in operation and decide whether to reduce, avoid, transfer, or accept the risk, following your organization’s own process and risk appetite. Link each treatment decision to an owner, a target date where action is needed, and a way to verify whether the control works.
Insurance can be one form of risk transfer, but a possible policy is not a substitute for technical, operational, or governance controls. Keep the assessment live: the UK toolkit includes monitoring and reporting as continuing parts of risk management, not one-time steps completed before buying coverage.
Set review timing and triggers appropriate to the use case. Revisit the register when the model or data changes, a vendor or integration changes, the scale or user population expands, the business purpose shifts, or an incident or meaningful performance change occurs. Track relevant measures such as errors, downtime, complaints, and system performance so that an earlier likelihood or impact judgment can be revisited against new evidence.
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The Federal Reserve’s Framework for the Supervision of Insurance Organizations discusses model inventory, validation, ongoing performance monitoring, and governance as practices for insurance organizations it supervises. It can provide context on insurer model risk, but it is not a universal requirement or checklist for every organization seeking insurance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should I ask a broker or insurer about AI coverage?
Bring your use-case inventory, prioritized scenarios, evidence and assumptions, controls, incident records, vendor information, and unresolved exposures. Then ask how the actual policy wording applies to the activities and losses you have documented; do not assume that an AI risk score maps to a coverage decision.
Use the quote and policy review to confirm the details that matter to your situation:
- Which legal entities, people, services, and AI-supported activities are insured?
- Which types of loss relevant to your scenarios are covered, and which are excluded or covered only under specific conditions?
- What limits, sublimits, deductibles, or retentions apply?
- How do claims reporting and defense arrangements work?
- Are claims-made or occurrence triggers relevant, and what dates or continuity terms apply?
- How are vendors, subcontractors, and third-party AI services treated?
- What geographic scope applies?
- How does the wording address AI-generated content or AI-supported decisions, if at all?
- What reporting duties or other conditions could affect a claim?
These are questions to verify against actual documents, not statements that the market has a standard answer. Compare offers using the same documented scenarios and inspect policy wording, not just a product label or summary. A licensed broker or insurer can address the applicable jurisdiction, business type, use case, and current terms; verify licensing and the relevant documents for your location.
Which rules apply, and whose AI responsibilities are they?
Regulatory material about insurers’ use of AI can help explain why governance, testing, documentation, and fairness matter in underwriting, but it should not be mistaken for an applicant’s universal checklist or a promise of coverage.
The NAIC’s Artificial Intelligence overview, last updated 3 April 2026, summarizes U.S. state insurance regulation and insurer practices. New York Department of Financial Services guidance addresses insurers authorized in New York, including risk-appropriate governance, actuarial support, testing for unfair discrimination, and vendor controls in underwriting and pricing. 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 consumer-impacting decisions remain subject to applicable insurance laws.
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Those materials concern regulated insurers and particular jurisdictions. They do not establish policy forms or terms available to every buyer. Your own obligations and potential coverage depend on applicable law and the policy issued for your circumstances.
What is the practical result of the assessment?
The useful outcome is a defensible, revisable record: each AI use is identified; plausible harm scenarios are specific; likelihood and impact judgments have definitions and evidence; controls and treatment decisions have owners; and open questions are ready for a broker or insurer. That record makes it easier to discuss risk clearly without treating an internal prioritization score as an underwriting answer.
Frequently Asked Questions
Does my business need ISO/IEC 23894 certification to get AI insurance?
The ISO page describes ISO/IEC 23894:2023 as guidance on AI risk management, not an insurance product or an insurance eligibility credential. The sources here do not establish certification to this standard as a requirement for purchasing coverage.
What if we have no AI incident history to use for scoring?
A lack of recorded incidents is not, by itself, proof that a scenario is unlikely. Make the evidence gap visible and use relevant testing, safe pilot observations, system metrics, and informed expert judgment where appropriate; update the estimate as operational evidence accumulates.
Do U.S. insurer AI regulations tell me what an AI insurance policy must cover?
The cited NAIC, New York, and Texas materials address insurer practices or legal obligations within their respective scopes; they do not establish a universal AI policy form for buyers. Coverage still has to be checked in the actual policy documents applicable to your jurisdiction.
Can the insurer’s AI governance checklist be used as my company’s application checklist?
Not automatically: insurer governance guidance is written for regulated insurance organizations and does not establish a universal applicant checklist. Your broker or insurer can identify the current application information needed for your business, location, and specific AI use.
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