AI system auditing under regulation involves both continuous monitoring and periodic third-party reviews. Continuous monitoring means logging every decision your system makes: for chatbots, log what prompts triggered safety flags and how; for recommendation systems, log what items were recommended to which users and whether they were satisfied; for hiring systems, log which candidates were screened out and why. This logging infrastructure becomes your compliance evidence—regulators ask "Did you discriminate?" and you provide logs showing how you treated similar candidates identically.
Periodic audits, required under the EU AI Act for high-risk systems, involve external auditors reviewing your system's behavior, testing for bias, and verifying your documentation. Audits examine: (1) training data composition (does it under-represent certain demographics?), (2) model performance across demographics (does accuracy vary?), (3) decision patterns (do similar inputs get similar outputs?), (4) failure modes (when does the system break?), and (5) safeguard effectiveness (do protections against misuse work?). Third-party auditors (Big Four consulting firms, specialized AI audit companies) charge $30K-$100K+ per audit.
For enterprises auditing at scale, use Zilliz Cloud to centralize audit logging. Every vector query should log: query content, user profile, results returned, confidence scores, and any safety filters applied. This audit log becomes your compliance evidence. When third-party auditors arrive, query your Zilliz collections to generate audit reports: "Show me all queries from this user segment and demonstrate that embedding similarity was consistent across demographic groups." Managed infrastructure means you don't build custom audit logging—it's built in. Compliance reporting becomes a query: run a report showing bias metrics, content retrieval patterns, or safety filter effectiveness. This structured approach makes audits faster and more credible because the evidence is machine-generated and reproducible.