Start by mapping what the workflow creates
An AI conversation may create more records than the chat visible on screen. There may be a prompt and response, uploaded documents, generated images or text, exports, files saved to a shared drive, and copies pasted into other tools. A team may also have workspace settings, connected apps or administrative records to consider. Retention rules work best when they cover these records separately rather than treating “the chat” as one item.
Make a short inventory of the AI tools you use and where their content goes. For each workflow, note who can access the material, whether it contains personal or confidential information, where useful outputs are saved, and who is responsible for deleting the rest. Check the tool’s actual settings and documentation before assuming that clearing a chat also removes uploads, connected-app copies or records held by the provider.
Keep records for a purpose, not by default
Choose a reason to retain each type of material. A finished article draft may be worth keeping in the publication’s normal file system. A prompt containing background notes may have no value once the draft is complete. A reusable prompt template can be saved after removing names, private details and project-specific data. If nobody can explain why a record is needed, that is a good reason to give it a short review period rather than keep it indefinitely.
A workable rule might be: review routine chats after a project ends; delete exploratory uploads once the needed output has been checked; retain approved deliverables in the team’s designated workspace; and review reusable templates periodically. Set an owner and a review date for each category. The exact periods should reflect the work and any applicable obligations, not an assumed universal deadline. If records must be preserved for a contract, dispute or other requirement, get appropriate guidance before deleting them.
Treat sensitive inputs differently
The simplest retention control is not to submit information the AI task does not need. Remove unnecessary names and identifiers, use a representative excerpt instead of a full file where possible, and consider whether a less sensitive description would produce a useful result. This reduces the number of copies that later need to be found and deleted.
For material that does need protection, establish stricter rules before use. Decide which tools are approved, who may upload files, whether sharing or connectors are allowed, and where the reviewed output belongs. Limit access to saved outputs as well as inputs: generated text can repeat details from a prompt, and an apparently harmless summary may still reveal confidential context. Deleting the source file does not make those details disappear from a copied output.
Make deletion a repeatable workflow
Write down the steps a person should take when a project closes. They might move approved outputs to the designated folder, remove unneeded drafts and local downloads, delete relevant chats and uploads through the tool’s controls, and check shared links or connected destinations. If a team uses several tools, give each one an owner rather than relying on a general reminder to “clean up AI data.”
Keep the checklist honest about what deletion means. Removing an item from the interface may not immediately erase provider-held records, backups, logs or copies made elsewhere. Available deletion controls and retention periods vary by service and plan. Read the applicable terms and settings, and avoid promising complete erasure unless you can verify it. Where confirmation matters, record what was deleted, where, by whom and when, without preserving the sensitive content in the deletion record itself.
Review the rules as workflows change
A short policy is more useful than a detailed one nobody follows. Put the categories, owners, review points and deletion steps in a place the team can find. Include a clear route for exceptions, such as a project that needs a longer-lived audit trail. Periodically test the policy with a real project: can someone locate its chats, uploads, outputs and shared copies, then tell which should still exist?
Revisit the inventory when you add a tool, enable a connector, change sharing settings or start handling a new kind of data. The goal is not to save nothing. It is to keep useful, approved work in known locations while limiting the accumulation of unnecessary AI conversations, files and outputs.
