What operational practices support compliance and auditability for AI systems?

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Maintain an AI system inventory (features, models, data sources, owners, risk level), immutable audit logs (who used the system, what versions produced which outputs, which tools acted, which humans approved), documentation (system cards, eval reports, risk assessments), change management records (approvals, eval results per release), data lineage (training, fine-tuning and RAG sources), access controls and periodic access reviews, retention and deletion processes, and incident records. Automate the evidence collection so audits don't become fire drills.

Building blocks

Practice	Implementation
AI inventory	Registry of features with owners, models, data classes, risk tier
Audit trail	Append-only logs: request ID, user, bundle version, tool actions, approvals, outputs (or hashes)
Change records	Release bundles linked to eval runs and approvers
Documentation	System cards, model cards, DPIAs, eval reports (generated from the pipeline where possible)
Lineage	Dataset versions → fine-tuned models; document sources → index versions
Access governance	RBAC, quarterly reviews, break-glass procedures with logging
Retention / deletion	Policy-driven TTLs; deletion workflows across logs, caches, datasets
Incident management	Records, root causes, corrective actions

Frameworks to align with (in coordination with compliance teams): internal model risk policies, ISO/IEC 42001 (AI management systems), the NIST AI RMF, and sector regulators and data protection laws relevant to the organisation (e.g. India's DPDP Act).

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What operational practices support compliance and auditability for AI systems?

Building blocks

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