How do you audit AI system decisions for regulatory compliance?

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

Expert answer

Maintain auditable records linking each significant AI decision to its inputs, model version, and any human review. These records make it possible to demonstrate compliance and reconstruct decisions during a regulatory inquiry.

What the security reviewer is checking

This question comes from buyers whose own regulators — under the EU AI Act, GDPR Article 22, sectoral rules in finance and healthcare, or internal model risk management standards — will ask them to account for decisions influenced by your AI. The reviewer needs to know whether you can reconstruct a specific AI output after the fact: what inputs and context produced it, which model and version ran, what the human reviewer did with it, and whether periodic reviews check outputs against quality and fairness expectations. They also look for regulatory mapping: someone at your company tracking which obligations apply to your AI use cases and translating them into audit procedures rather than waiting for customers to ask.

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.

AI outputs in our platform are traceable end-to-end, which is the foundation our compliance auditing builds on. For each AI-assisted result we retain the correlation between the output, the model and version that produced it, the source content it drew from, and the subsequent human action — accepted, edited, or rejected — so a specific decision can be reconstructed months later from audit records. Because our AI is assistive, a human remains the decision-maker of record, keeping customers clear of fully automated decision-making obligations under GDPR Article 22. On a scheduled cadence, our quality program samples production outputs and evaluates them against accuracy and grounding criteria, with results reviewed by the AI governance committee and regressions tracked to remediation. Our legal and security teams jointly maintain a regulatory mapping covering the EU AI Act’s risk classification of our use cases and applicable privacy obligations, refreshed as guidance evolves, and audit artifacts — traceability records, sampling results, and governance minutes — are available to support customer and regulator inquiries.

Evidence reviewers expect you to attach

AI governance framework or policy including the audit and sampling procedure
Example traceability record linking an output to model version and source inputs (sanitized)
Output quality review results or governance committee minutes (sanitized)
Regulatory applicability assessment for your AI use cases (e.g., EU AI Act classification)

Follow-up questions reviewers ask next

Can you reconstruct a specific AI output from six months ago, including its inputs?
How have you classified your AI use cases under the EU AI Act?
What sampling methodology and cadence does your output review use?
Would your traceability records be available to our regulator on request?
Who owns regulatory tracking for AI obligations at your company?

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