AI-assisted automation can prepare drafts, organize information, or perform bounded routine steps. It needs a clear authorization, data boundary, and accountable human owner.
Scope note: This is a vendor-neutral governance checklist. It does not claim that any product provides AI, automations, settings, approvals, predictions, integrations, access controls, metrics, pricing, performance, or outcomes.
State the approved purpose
Document the narrow use case, inputs, allowed outputs, owner, review point, escalation path, and stop condition. Do not start with irreversible, urgent, safety-related, legal, financial, employment, access, privacy, or customer-impacting decisions.
Set data and access limits
Use only the minimum necessary authorized data. Do not use real names, contact details, addresses, account IDs, payment details, credentials, tokens, internal links, screenshots, recordings, logs, or attachments in public examples or unapproved testing.
Restrict who can configure, review, change, pause, or revoke the workflow.
Require review before material use
Treat each output as a draft. A system event, status, or generated recommendation is not proof of authorization, accuracy, completed work, payment, collection, customer agreement, or real-world result.
Test with fully synthetic cases
| Condition | Expected control |
|---|---|
| Clear, low-risk request | Reviewable draft or bounded action |
| Missing context | Pause for clarification |
| Sensitive or urgent case | Human escalation |
| Incorrect output | Stop, correct, and record |
| Unauthorized access or data | Restrict, revoke, and review |
Monitor exceptions, not vanity metrics
Review errors, unexpected actions, data exposure risks, access changes, corrections, and user feedback. Do not present activity counts or model output as evidence of ROI, reliability, savings, accuracy, or business performance.
Reauthorize regularly
Set a review date for the purpose, data use, access, exception handling, retention, and rollback. Pause the automation when any boundary is unclear.
Discussion
Which routine step could your team test safely with completely synthetic data first?