AI Output Review Workflow
Define a structured, documented process for reviewing AI outputs before they are acted upon or distributed.
Objective
Ensure AI outputs are systematically evaluated for accuracy, bias, and policy compliance before operational use.
Maturity Levels
Initial
Review is ad hoc; no defined steps, roles, or acceptance criteria exist.
Developing
Review steps are informally agreed within teams but not documented or consistently followed.
Defined
A documented review workflow specifies who reviews what, using which criteria, with a clear accept/reject/escalate path.
Managed
Review cycle times, rejection rates, and escalation frequency are tracked and reported.
Optimizing
Review patterns feed continuous improvement of model prompts, guardrails, and output validation rules.
Evidence Requirements
What an auditor or assessor would expect to see for this control.
- —Documented review workflow procedure with defined acceptance criteria and reject/escalate paths per use case
- —Review completion logs including disposition (accept/reject/escalate), reason codes for rejections, and reviewer identity
- —Rejection rate statistics by use case over a defined reporting period
- —Role assignment records confirming reviewer and requestor are separate individuals for high-risk outputs
- —Reviewer training records confirming familiarity with acceptance criteria and reason code taxonomy
Implementation Notes
Key steps
- Separate the reviewer role from the requestor role — the person who prompted the AI should not be the sole reviewer of its output.
- Define acceptance criteria explicitly: what constitutes a passable output for each use case? Ambiguous criteria lead to inconsistent reviews.
- For generative AI: require reviewers to check factual claims against sources, not just scan for obvious errors.
- Document rejections with reason codes — this data is essential for model improvement and audit defense.
Example Implementation
Legal team using AI to draft first-pass contract summaries
Contract Summary Review Checklist
Reviewer must confirm all items before approving output for use:
- All parties named correctly (verify against source document)
- Key dates present and accurate: execution date, expiry, notice periods
- Obligations section covers all material commitments
- Defined terms match source document definitions
- No hallucinated clauses — spot-check 3 specific claims against source text
- Jurisdiction and governing law correct
Rejection reason codes: FACTUAL_ERROR | MISSING_MATERIAL | HALLUCINATION | FORMAT | OTHER
Rejections must include: reason code · specific passage flagged · brief correction note
Control Details
- Control ID
- HOC-003
- Domain
- Human Oversight
- Typical owner
- Business Line / AI Governance Team
- Implementation effort
- Low effort
- Agent-relevant
- No