A Team Policy for Managing ChatGPT and Claude Conversations
Create clear team rules for AI conversation access, project naming, exports, approved tools, privacy, and retention across ChatGPT and Claude.
A practical team AI policy names approved services, defines allowed data, assigns workspace owners, and explains how conversations are organized, retained, exported, and deleted.
Define which tools and data are approved
List the AI services and account types employees may use, and specify which data classes can be entered. Consumer and managed workspaces can have different controls. Explain how staff should handle client information, credentials, personal data, regulated material, and source documents.
Identify who approves new browser extensions and connected storage services. Require a review of permissions, vendor terms, and privacy practices before an extension is installed on a work device. A product name alone does not tell a team how content is processed.
Set shared organization rules
Choose a project naming pattern, ownership convention, and place for approved reference material. Keep client or department contexts separate where access rules differ. Explain when a chat should be archived, when it should be deleted, and what should be captured in a durable decision record.
A good policy avoids prescribing a complicated label for every prompt. Focus on retrieval and accountability: make important work findable, make a person responsible for final decisions, and keep source links near facts that must be checked.
Govern exports and backups
Define who may export account data, where it may be stored, how often it is reviewed, and how long archives are retained. Account exports can contain personal and company information. Use approved storage with restricted permissions and multi-factor authentication; do not default to a personal drive.
Test recovery for one sample project and document who can restore access if an account owner leaves. Coordinate backups with deletion requirements so a removed record is not silently preserved forever in a duplicate archive. Consult legal or security teams for regulated data.
Train staff and review the policy
Give employees examples of safe and unsafe use, a quick way to report accidental sharing, and a contact for approval questions. Explain that model output is a draft until verified and that external sources should support important claims.
Review the policy when providers change settings, the company adds an AI service, or incidents reveal gaps. Ask staff where retrieval still fails and what extra records create unnecessary risk. A policy is useful when teams can follow it in everyday work and owners update it when practice changes.
Frequently asked questions
Should a team use one shared AI account?
Choose an account structure that supports individual accountability, access controls, and the provider's terms. Do not share credentials when a provider offers managed access.
Who should own AI conversation backups?
Assign a named workspace or records owner and a backup contact, with access limited to people who need it.