How Long Should You Keep AI Prompt Logs?

Prompt logs should be kept only as long as they serve a purpose you can name, which for most companies means thirty to ninety days for operational logs and longer only for the specific systems where you need an audit trail of decisions. The default in most organizations is indefinite, because nobody made a decision at all.

That default is a problem. A prompt log is a verbatim record of what employees typed, which routinely includes customer names, contract terms, medical details, and whatever was in the document they were summarising. It is often the most sensitive dataset a company created last year, and it sits outside every retention schedule written before it existed.

What are you actually keeping, and why?

Separate the purposes, because they justify very different periods.

The important insight in that table is that only one row needs full prompt content for a long time, and it is the narrow one covering systems that make or influence decisions about people. Everything else can be met with metadata, samples, or shorter windows.

Most companies keep everything for the sake of the third row, applied to systems that are not in the third row.

Are prompt logs personal information?

Frequently, yes, and in two directions at once.

They contain personal information about the people mentioned in the prompt, who never consented to appear there and often do not know the system exists. They also contain personal information about the employee who wrote it, which brings employee privacy expectations into play, particularly in Quebec and for federally regulated employers.

Both directions have consequences. An access request from a customer may reach into prompt logs if the customer is identifiable in them. A workplace monitoring analysis applies to the employee-facing side, because a complete record of everything a person asked an assistant is a rich behavioural dataset. Neither of these is a reason to log nothing. Both are reasons to decide deliberately.

How do you set a period you can defend?

Work backwards from the latest legitimate use, then subtract.

Ask what the longest genuine purpose is for this specific system. For a drafting assistant, the honest answer is usually operational: you need last month to investigate a complaint, and nothing after that. For a claims triage tool, the honest answer includes the appeal window, because you may need to show what the system saw when a decision is challenged.

Then apply three reductions. Strip content you do not need, keeping metadata where metadata answers the question. Sample rather than retain in full for quality work. And separate the retention of the decision record from the retention of the raw prompt, because the first often needs years and the second rarely does.

Write the result into your retention schedule as a named category rather than leaving it to inherit a generic application-log rule that was written for something else entirely.

What about the vendor's copy?

This is the part teams forget. Your retention decision governs your logs. The model provider and any intermediate vendor keep their own, on their own schedule, which is frequently thirty days and occasionally much longer for abuse monitoring.

Three things to establish and record against the entry in your vendor inventory: the vendor's retention period, whether it can be shortened or set to zero on your tier, and whether their staff can view content during that window. A vendor who cannot answer clearly should not be receiving personal information.

Deleting your copy while the vendor holds a longer one does not give you the outcome you told people you would deliver, and that gap tends to surface during an access request rather than during a review.

Where do the logs actually live?

More places than the obvious one. The application log, the model provider's account, an observability platform, an error tracker that captured a full request payload, and the analytics export. Each of those is a separate retention decision, and error trackers are the one that catches people, because they routinely capture more context than anyone intended.

Add prompt logs to your data flow map as a flow in their own right. They behave like one, and they are invisible on most existing maps because the map predates the feature.

For the broader discipline, building a defensible data retention schedule covers how the rest of the schedule should work.

This is general information, not legal advice.

Valdra treats AI logs as their own retention category, tied to the system that produces them and the vendor that receives them, so the period you set is the period that actually applies.