AI Usage Policy Template for Teams Using ChatGPT and Claude
A useful AI policy is not a page that says “use AI responsibly.” It tells people which work is fine, which data needs a safer path, who decides the rules, and how those rules are applied when work gets busy.
What an AI usage policy needs to decide
The policy should answer the questions employees face in the moment: which AI tools are approved, what work can be shared, what data must be removed first, and who to ask when a use case is unclear.
A policy also needs a clear enforcement model. A credential and a customer email should not necessarily trigger the same response. The action should match the risk and the job being done.
- Approved and unapproved AI tools, including whether personal accounts are allowed for work.
- Data categories that may never be shared, such as credentials, private keys, client records, payroll data, and regulated identifiers.
- Data categories that can be used only after redaction or approval.
- Who owns the policy and how employees get help with unclear cases.
- How the company will review policy evidence without collecting prompt content.
A practical policy template
Start with a simple rule: use AI to improve work, but do not send data that the company would not send to an external third party. Then define the exceptions and safer alternatives instead of expecting people to interpret a broad warning alone.
- Allowed: public information, synthetic examples, approved templates, and work that contains no client or production data.
- Review or redact first: customer communications, contracts, support cases, internal financial discussions, and production logs.
- Never send: passwords, API keys, private keys, access tokens, database connection strings, unredacted personal data, and confidential source code where the policy prohibits it.
- If unsure: pause, remove identifying values, or use the internal escalation route before sending.
Make the policy run where work happens
A written policy is the decision layer. Teams also need a control at the actual point of use: in a supported AI chat or configured coding workflow. That is where a policy can warn, redact, block, or simply record a metadata-only event.
Start in observation mode. Review the categories that appear during normal work, then turn on stronger actions only where the evidence justifies them. This avoids punishing ordinary work with rules that were never tested.
A seven-day rollout that does not create backlash
Tell employees plainly what the company is doing: the purpose is to prevent accidental sharing, not to read their prompts. Begin with a short observation period, show the team what categories were seen, and explain the first rules before switching on enforcement.
Use different policies for different departments. Finance may need stronger personal-data controls. Developers may need credential and connection-string controls. Support may need customer identifiers redacted instead of blocked so they can still draft a reply.
What clients and auditors will ask
Be ready to show which AI tools are covered, what the policy actions are, where coverage has limits, and evidence that the controls ran. Do not claim that a policy covers every desktop app, browser, or AI site unless the relevant device coverage says it does.
The credible answer is a policy plus a clear coverage record, not a PDF policy alone.
Related resources
Questions
What teams ask before they roll out a policy.
Should we ban ChatGPT and Claude?
Usually no. A workable policy separates low-risk use from sensitive-data handling. Begin with clear guardrails and approved workflows rather than a ban that people will work around.
How often should an AI usage policy be reviewed?
Review it when your approved tools, data categories, or workflows change. A short monthly policy-evidence review is more useful than leaving a document untouched for a year.
Can different departments use different rules?
Yes. Department-scoped policies let teams apply the same overall standard while matching the data and workflow risks of finance, support, engineering, or operations.
See how the policy fits your actual workflow.
In 20 minutes, we can map your tools, data categories, and a safe observation-first rollout.