AI Governance defines who makes decisions about AI, which uses are permitted, and what evidence supports those decisions. It connects business purpose with security, privacy, legal, and operational responsibilities.
Translate decisions into enforceable controls.
A governance statement such as 'a person approves external communication' must become a concrete workflow. Identify the approver, what they review, where approval is recorded, and which component checks it before execution. The policy and the implementation should describe the same boundary.
Governance artifacts can include a use-case record, data inventory, permission model, evaluation results, and decision log. Their purpose is to support accountable decisions, not to create a separate paperwork process that no longer reflects the deployed system.
Separate roles and consequences.
Business owners define purpose and acceptable outcomes. Technical owners implement and operate the solution. Security, privacy, and legal stakeholders review relevant risks and obligations. A change that affects sensitive data or external commitments may need a different approval path from an internal drafting improvement.
Avoid allowing the same automated component to propose an action, widen its own access, and declare the result acceptable. Separation of duties matters where an incorrect action would be difficult to reverse or could affect people outside the team.
Example: changing an agent's capability.
A read-only agent might initially answer questions about approved customer records. Adding a tool that updates those records changes its risk profile even if the chat interface looks unchanged. The change should trigger review of authorization, evaluation cases, logging, recovery, and operational ownership.
Useful evidence would show permitted updates, denied updates, failed approvals, and the behavior after an interrupted action. Recording the exact capability and configuration helps reviewers connect an approval to what was actually released.
Keep evidence aligned with the lifecycle.
Review models, sources, integrations, permissions, and exceptions when they change. Assign responsibility for pausing or retiring the use case and revoking its access. Agent-management and compliance tools can support these activities, subject to product coverage, but cannot determine an organization's risk acceptance or establish regulatory compliance without the surrounding processes.
Further reading
For purpose, ownership, action approvals, and lifecycle review, read AI Governance: boundaries before tools.