High-Risk AI Audit Trail
Added May 2026
Keep tamper-evident audit trails for AI in regulated domains. Cover the lifecycle from input through decision to outcome.
Objective
Provide regulators, auditors, and affected individuals with verifiable evidence of how AI-driven decisions were made and on what basis.
Maturity Levels
Initial
No audit trail exists for high-risk AI decisions.
Developing
Partial audit records exist but are not tamper-evident and do not cover the full decision lifecycle.
Defined
A complete audit trail is maintained covering inputs, model selection, outputs, human review steps, and final outcomes.
Managed
Audit trail integrity is verified periodically; completeness gaps are tracked and remediated.
Optimizing
Audit trail generation is automated and tested; records meet documented regulatory requirements validated by legal.
Evidence Requirements
What an auditor or assessor would expect to see for this control.
- —Complete audit trail record for a sample of decisions, confirming all required components are present (AI output, input context, human review, outcome)
- —WORM (write once, read many: records cannot be edited once saved) or equivalent tamper-evident storage (storage that reveals any alteration) configuration evidence with access control documentation
- —Integrity verification records showing periodic hash checks (automated checks confirming records have not been altered) were run and passed
- —Quarterly reconstruction exercise records demonstrating sampled decisions can be fully reconstructed from audit records
- —Regulatory requirement mapping confirming audit trail design satisfies each applicable legal obligation
Implementation Notes
Key steps
- Scope 'high-risk' using your risk classification (HOC-001), audit trail requirements should be proportionate to system risk tier.
- Include the human review record alongside the AI output: who reviewed, when, what they saw, and what decision they made.
- Store audit records separately from day-to-day operational logs with stricter access controls and longer retention periods.
- Test audit trail completeness before regulatory exams, not during, run a dry-run reconstruction of a sample of past decisions.
Example Implementation
EU-regulated financial firm using AI for automated trading signals reviewed by human traders
High-Risk AI Audit Trail: Trading Signal System
Audit record components (per decision):
- AI output record, signal type, asset, confidence, model version, timestamp
- Input context record, market data snapshot hash, prompt version, retrieved context references
- Human review record, trader ID, review timestamp, decision (execute / modify / reject), rationale code
- Outcome record, whether trade was executed, execution details, outcome (appended post-execution)
Tamper-evidence: All records written to WORM (write-once read-many) storage; SHA-256 hash of each record stored in a separate integrity log
Access controls: Read access: Compliance, Legal, Regulators (on request). No write or delete access for any operational role.
Retention: 10 years from decision date (EU AI Act Art. 12; MiFID II record-keeping requirement)
Pre-audit test: Compliance team runs quarterly reconstruction exercise, selects 10 random past decisions and verifies all four record components are present and consistent