AI Infrastructure Security and Compliance Self-Assessment

An AI infrastructure security and compliance self-assessment maps controls to requirements, identifies gaps before an auditor finds them, produces the evidence each control needs, and prepares the organization for audit — because discovering gaps during the audit is a finding. For the audit methodology, see auditing AI infrastructure providers. For the standards framework, see AI security compliance standards.

The Self-Assessment Method

1. Map controls to requirements: for each applicable framework, list the controls required and map them to the AI infrastructure layers — compute, storage, network, platform — that satisfy them. A gap at any layer is a finding waiting to happen. 2. Test each control: verify the control is operating, not just configured. Isolation is not just claimed; it is tested. Encryption is not just enabled; it is verified across all surfaces. Access is not just defined; it is tested across roles. 3. Produce evidence: for each control, generate the logs, configurations, and test results the auditor will request — before the auditor requests them. 4. Close gaps before audit: fix the gaps the self-assessment found, because the auditor will find them too. For the evidence preparation framework, see how compute stacks match compliance audits.

FAQ

How do I self-assess AI infrastructure compliance?

Map controls to requirements, test each control, produce evidence, and close gaps before the auditor arrives. A self-assessment is the pre-audit that prevents findings. See the four steps above.

Summary

AI compliance self-assessment maps, tests, evidences, and closes gaps. For the full framework, see auditing AI providers.