An AI use-case risk assessment examines the intended purpose, affected people, data, permissions, failure modes, safeguards, and remaining uncertainty of a specific AI-enabled workflow. Assess the complete system and its consequences—not only the model’s accuracy or the vendor’s product label.
A tool that proposes a refund and a tool that issues a refund can produce identical text while carrying different authority. This guide shows how to document that distinction and reach a release decision. It supports our AI transformation governance guide and operating-model workflow.
Start with a bounded use-case intake
Record the user need, business owner, people affected, data sources, system components, available tools, expected benefit, and excluded uses. Describe where human judgment enters the process and what happens when the system cannot complete a task.
| Field | Example |
|---|---|
| Purpose | Draft answers to policy questions for support staff |
| Users and affected people | Support employees and customers whose records are retrieved |
| Data boundary | Approved policy documents and purpose-limited customer fields |
| Initial action boundary | Draft only; no external sending, account changes, or payments |
| Known failure | Retrieving a stale policy or another customer’s information |
| Fallback | Manual policy lookup and normal support escalation |
A clear exclusion is as useful as a permitted use. If the assistant has not been assessed for eligibility decisions, do not let an adjacent workflow quietly turn its drafts into those decisions.
Review risk dimensions without hiding serious consequences
- People and consequences: who can be affected, how severely, and whether they can identify or challenge an error.
- Data and exposure: sensitivity, access authorization, source quality, retention, and the possibility of cross-user disclosure.
- Authority: actions available to the system, scope of credentials, downstream effects, and reversibility.
- Reliability: realistic failure cases, representative evaluations, and performance differences across relevant conditions.
- Operation: monitoring, reviewer capacity, response time, fallback, and ownership.
- External obligations: duties arising from the actual jurisdiction, sector, contracts, role and use.
Do not average a potentially severe privacy or safety consequence away because several other dimensions look low. Use mandatory review triggers for consequences that require specialist judgment. The triggers must reflect the context; this guide does not assign a legal risk category.
A worked example: expanding from drafting to action
Suppose the support team wants the assistant to issue refunds rather than only draft advice. The original quality evaluation remains useful, but it does not establish that the new action is authorized, properly limited, or recoverable.
| Question | Draft-only workflow | Refund-capable workflow |
|---|---|---|
| What can go wrong? | Staff may use an inaccurate draft | The system may transfer value or alter a record incorrectly |
| What must be enforced? | Access to the right records and clear draft status | Identity, eligibility, approval boundaries, transaction limits and duplicate-action protection |
| What must be observable? | Evidence used and staff corrections | Requested action, authorization result, actual outcome and reversals |
| What can stop it? | Disable drafting and use manual lookup | Revoke execution permissions while preserving a safe manual path |
OWASP’s 2026 excessive-agency guidance supports separating tool functionality, permissions, and autonomy. A model’s apparent confidence is not an authorization mechanism. Enforce the boundary in application or downstream logic. [1]
Define evidence that can change the decision
Create tests that resemble normal work and tests that challenge the boundary. Include missing or contradictory source facts, unauthorized access, misleading documents, invalid tool inputs, repeated actions, and a failed downstream service.
For each test, state the expected behavior and what would prevent release. If the system should refuse a request or escalate it, evaluate whether it does so; do not score only the quality of successful answers.
Examine relevant cohorts and operating conditions. Microsoft’s Responsible AI Toolbox demonstrates techniques for exploring error distributions and group-level differences. Select metrics suitable for the actual use case rather than assuming one aggregate accuracy measure is sufficient. [3]
Record residual risk and the authority to accept it
For an unresolved issue, record the scenario, affected people, existing safeguards, remaining uncertainty, accountable owner, and decision. A risk acceptance should state its scope, conditions and review trigger; it should not read as a permanent exemption for every future version.
Possible outcomes include a bounded pilot, a narrower capability, additional evidence, a manual-only path, or a decision not to deploy. “We bought the vendor’s enterprise plan” is procurement information, not a complete risk acceptance.
Carry the assessment into production
Translate failure scenarios into operational signals. For stale-policy risk, record the source version and retrieval date. For unauthorized execution risk, record authorization decisions and action outcomes. For misleading output, keep a route to review the evidence and correct the result.
OWASP’s 2026 misinformation guidance distinguishes fluent output from verified information and recommends checking claims and state before action. Monitor critical omissions and unsupported assumptions as well as explicit false statements. [2]
A prompt, model, source, tool or scope change can invalidate an earlier assumption. Connect the assessment to the material-change review process so it does not become a detached spreadsheet.
Risk-assessment questions
Is an AI risk assessment the same as a model evaluation?
No. Model evaluation supplies technical evidence. A use-case risk assessment also examines intended use, affected people, data, authority, operational controls, obligations, and residual risk.
Can we reuse a vendor’s risk assessment?
It can inform your review, but your users, data, configuration, permissions, and downstream workflow may differ. Verify the parts that determine your actual exposure.
Should a risk score automatically approve a use case?
Not by itself. A numeric score can help organize review, but mandatory triggers and specialist judgment should address consequences that an average might hide. Record the evidence and the decision authority.
Sources and scope
- OWASP GenAI LLM Top 10 2026: Excessive Agency.
- OWASP GenAI LLM Top 10 2026: Misinformation.
- Microsoft Responsible AI Toolbox.