AI Governance

A practical example

Example: before enabling an AI agent that emails customers, a governance process requires a documented use-case review, legal sign-off on data usage, a human approval step for outbound messages, and quarterly audits of agent actions.

What to evaluate before investing

• Ask what governance features the platform provides natively: role-based access, approval workflows, action logs, and policy enforcement points.
• Verify whether vendor and model changes are versioned and documented, so you can reconstruct what any agent did and with which model.
• Confirm how the vendor supports data residency, retention, and training-use restrictions for your data across all integrated models.

Limitations and tradeoffs

Governance adds friction by design; overly heavy approval processes can stall useful automation, so calibrate oversight intensity to the risk of each use case rather than applying one policy to everything.