How to govern employee AI use without blocking the business

A sales team wants to summarize customer calls. Engineering wants help reviewing code. Finance wants to explain a spreadsheet. One company-wide rule saying "do not upload sensitive information" leaves each team to interpret a different risk.

Start with the work people want to do. Ask what enters the tool, what it can retrieve through connectors, and where the result goes. The same AI product may be acceptable for rewriting public copy and unsuitable for processing confidential deal terms under its current settings.

Write one usable approval record

For a hypothetical customer-call summarization workflow, an approval record could say:

• The sales operations owner is responsible for the workflow and access list.
• Only the managed company workspace may be used.
• Recordings require the organization's applicable recording and privacy approvals.
• Payment details, credentials, and restricted customer records must be excluded.
• CRM access is limited to the approved records; automatic updates remain disabled during the pilot.
• A salesperson checks the summary against the recording before saving it.
• The owner records retention, deletion, and vendor processing settings, with a review date.

That record gives IT something to configure and employees something to follow. Avoid promising that an enterprise subscription automatically prevents training use or guarantees deletion. Check the terms and settings for the actual service and account tier.

Check the connector before approving the assistant

A tool with no pasted secrets can still retrieve sensitive material through a connected drive, mailbox, or repository. Review scopes, service identities, shared folders, and who can enable additional integrations. Test with an ordinary employee account, not only an administrator.

Use a deliberately restricted test document to check the access boundary. Record whether the assistant can retrieve it and whether the relevant audit events appear. A refusal in the chat interface is not a substitute for removing excessive permissions at the data source.

NIST's Generative AI Profile covers risks including data privacy, information integrity, and human reliance on generated results. Use those categories to ask concrete questions about each workflow; the profile is not a product approval list.

Keep review proportional to the consequence

Drafting an internal lunch announcement needs little oversight. Sending customer advice, interpreting a contract, changing production code, or initiating payments needs an accountable reviewer with access to the underlying evidence.

Set that requirement before deployment. Name who checks factual claims, who approves an external message, and which actions the tool cannot take autonomously. For AI agents with access to business systems, review write permissions and approval boundaries separately from ordinary chat use.

Give exceptions a workable route

Ask requesters for the task, data categories, proposed tool, business benefit, and deadline. Offer an approved alternative when possible. Assign a decision owner so requests do not disappear into a security mailbox.

For accidental uploads, tell employees where to report the tool, account, time, and data involved without reposting the sensitive material in the ticket. The response owner should check access, retention, deletion options, and any exposed credentials. Legal or privacy staff can assess obligations when necessary.

Review usage with department owners. Look for abandoned pilots, excessive connector permissions, repeated exceptions, and outputs requiring substantial correction. Collect only monitoring data needed for the stated purpose. The next policy revision should address those observed problems, not add another page of warnings.