AI governance defines how AI decisions and actions are controlled, evaluated, and owned. Product teams make it tangible through understandable permissions, visible evidence, meaningful confirmations, and recovery paths.
From policy to behavior
NIST’s AI Risk Management Framework provides a voluntary foundation for managing AI risks. It is a useful reference for organizing the conversation. My recommendation is to translate that conversation into specific user journeys and observable system behavior.
A policy saying that an agent must protect customer data is incomplete as a product requirement. The team needs to define which users can request which actions, what information the agent can access, and how denial or uncertainty is communicated.
Design around consequential actions
Make a distinction between reading information, preparing a change, and committing a change. Those actions may require different permissions even when they happen in the same conversation.
Imagine an assistant preparing a refund. It should make the proposed amount, account, and rationale clear before the commitment step. The required approval should follow the organization’s actual policy. This example illustrates an interaction pattern, not a universal permission rule.
Make trust inspectable
Show relevant sources, the scope of access, and the status of an action. Avoid a generic message that says everything is done when the agent only prepared a draft. A user should be able to understand what happened and what remains.
Product teams should also make the unhappy paths usable. Explain permission denials without exposing sensitive information. Provide a path to the right owner. Keep evidence that supports investigation when an action fails.
Measure the governed experience
Track task completion alongside unauthorized attempts, recoverability, and user confusion. Review where confirmations add safety and where they create repeated interruptions without reducing risk.
Governance should help people act with confidence. A blocked user is not automatically a protected user; a successful action is not automatically an authorized one. Test both the control and the experience around it.
Questions worth asking
Who owns AI governance in a product team?
Ownership is shared across relevant functions. The product team should name accountable owners for each consequential workflow and make the controls visible in the experience.
Should every AI action ask for confirmation?
No. Match approval requirements to permissions, consequences, reversibility, and organizational policy.
Sources & further reading
These sources inform the context. Recommendations and frameworks are editorial analysis, not claims of proven results. AI-assisted draft prepared for Yaron’s review; personal experience claims require his confirmation.