Private AI Storage Governance Checklist for Compliance
Private AI storage governance is the set of controls — access, encryption, residency, retention, deletion, and audit — that protect regulated data across every storage surface the AI workload touches. For the storage architecture requirements, see AI storage architecture requirements. For the full compliance framework, see enterprise AI compliance and residency.
The Governance Controls
Access control: who can read, write, or delete each storage surface — least-privilege, time-bounded, and logged. The access model must cover not just the primary dataset but checkpoints, inference logs, and vector databases. Encryption governance: at rest and in transit, with key residency matching data residency, and customer-managed key options where policy requires. Residency governance: every storage surface must reside within the permitted boundary, with evidence of the physical location and controls preventing out-of-region access. For the residency requirements, see data residency compliance checklist. Retention and deletion: data must be retained for the required period and deleted when retention expires, with deletion verified and logged. Incomplete deletion — data in snapshots, backups, or caches — is a governance gap. For the deprovisioning process, see AI workload deprovisioning security. Audit logging: every access, movement, and lifecycle event must be logged, tamper-resistant, and available for auditor review.
Governance checklist
| Control | What to verify | Evidence |
|---|---|---|
| Access control | Least-privilege across all surfaces | IAM configuration, access logs |
| Encryption | At rest and in transit; keys bounded | Encryption scope, key residency docs |
| Residency | All surfaces within boundary | Location docs, data path map |
| Retention/deletion | Policy enforced; deletion verified | Retention policy, deletion verification logs |
| Audit logging | All access and movement logged | Tamper-resistant logs, audit trail |
FAQ
What does AI storage governance need to cover?
Access control, encryption, residency, retention and deletion, and audit logging — across every AI storage surface (datasets, checkpoints, logs, vector databases). The governance gap is usually the AI-specific surfaces that traditional storage governance misses. See the checklist above.
How do I prepare AI storage for a compliance audit?
Have evidence for each control: access configuration and logs, encryption scope and key residency, physical location and data path map, retention and deletion policies with verification, and tamper-resistant audit logs. The evidence must exist before the audit. See enterprise AI compliance.
Summary
Private AI storage governance requires access control, encryption, residency, retention/deletion, and audit logging across all AI storage surfaces — verified with evidence. For the full governance and compliance framework, see enterprise AI compliance.
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