What information enters the system?
Understand the source, sensitivity, permitted uses, and retention of information before sending it to an AI service. Publicly available data is not automatically appropriate for every purpose.
Ask which provider settings and agreements apply, who can access the information, and what happens when an employee includes material that should not have been shared.
Who reviews consequential outputs?
Human oversight needs a defined task, not just a statement that a person is in the loop. Identify what the reviewer checks, what evidence they can see, and whether they have the time and authority to intervene.
Higher-impact decisions call for appropriate specialist review. A model's fluent answer is not evidence that it is accurate, fair, or legally suitable.
What can tools and agents do?
An assistant that drafts text has a different risk profile from one that changes records or sends messages. Bound permissions, require approval where consequences warrant it, and test failure paths before granting wider access.
Consider whether a simpler deterministic workflow is sufficient. More autonomy is not automatically more useful.
Who owns the ongoing review?
Assign responsibility for changes in models, tools, data, and workplace use. Make escalation and incident handling understandable to the people using the system.
This editorial provides general questions for discussion. It is not legal advice or a statement that a particular organization meets any compliance standard.