What AI risk assessments do you perform?

How to answer this security questionnaire question, with an expert response your security or GRC team can adapt.

Expert answer

Conduct AI-specific risk assessments covering data exposure, model misuse, bias, and availability before launch and when models change materially. Map identified risks to controls and track them alongside your enterprise risk register.

What the security reviewer is checking

This question checks whether AI risk management is a process or an improvisation. Reviewers want to know what triggers an assessment (new AI feature, new model, new provider, materially changed use of data), what dimensions get evaluated — privacy impact, security exposure, bias and fairness where decisions affect people, hallucination and misuse potential, and third-party model risk — and who reviews and accepts the findings. Alignment to a recognized structure such as the NIST AI Risk Management Framework or ISO/IEC 42001 signals maturity, but the reviewer mostly wants proof that assessments actually happened and produced decisions, including features changed or rejected as a result.

Example response you can adapt

This is an illustrative template, not a real vendor's security posture. Replace every claim with what is actually true for your organization before submitting it.

Every new AI feature, model change, or new AI provider goes through a structured AI risk assessment before release, and existing features are reassessed annually or when their data usage materially changes. The assessment, modeled on the NIST AI Risk Management Framework functions, evaluates the data involved and its classification, security exposure of the new pipeline (including prompt injection and data exfiltration paths), output risks such as hallucination or unsafe content in the specific use context, the degree of automation versus human review, and the provider’s terms on retention and training. Third-party models are additionally screened through our vendor risk process. Each assessment produces documented findings, required mitigations, and a residual-risk decision signed off by security and product leadership; features have shipped with added guardrails — and in some cases been declined — as a direct result. Assessment records are retained and available for audit.

Evidence reviewers expect you to attach

AI risk assessment template or procedure document
A completed assessment for a shipped AI feature (sanitized)
AI governance policy naming the accountable reviewers and sign-off flow
Vendor risk assessment for a model provider

Follow-up questions reviewers ask next

Can you share a completed AI risk assessment for a feature we would use?
How do you assess prompt injection and data exfiltration risk specifically?
Have any AI features been rejected or modified because of an assessment?
How often are deployed AI features reassessed?
Do you evaluate bias or fairness where AI outputs affect individuals?

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