Named owner
Identify who owns the AI-enabled workflow, who can approve changes, and who receives operational issues.
AI governance
Give AI systems clear ownership and operating boundaries
Practical AI governance is not a badge. It is the discipline of making purpose, data, permissions, evaluation, human review, change control and incident ownership visible around the real workflow.
Purpose boundary
Define the user task and what the AI feature must not decide or do outside the intended workflow.
Data boundary
Classify inputs, retrieved knowledge, logs and generated outputs before choosing providers or retention behavior.
Provider boundary
Document which model/provider processes which data, the integration path, fallback behavior and dependency assumptions.
Access & tools
Use least privilege for connected tools, APIs and knowledge sources; require stronger controls for consequential actions.
Evaluation
Define representative cases, acceptance criteria and known failure categories before shipping meaningful changes.
Human oversight
Specify where a human must review, approve, correct or take over the workflow.
Change control
Treat model, prompt, retrieval, tool and provider changes as product changes that can alter behavior.
Monitoring & incidents
Define useful signals, escalation paths and rollback/disable options without logging unnecessary sensitive content.
This page is general engineering guidance. It is not legal advice, a regulatory conformity assessment, a certification, or proof that a specific AI system is safe or compliant.
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Need to turn AI governance into implementation work?
Describe the workflow, users, data, model/provider, connected tools, human review and operational owner in the Project Planner.