An audit trail and a retention plan are not the same thing
Teams that build a real audit trail for their AI workflows, capturing the evidence a decision drew on, the rule or prompt version applied, and the reviewer's action, solve the visibility problem. They can answer what happened and why for any single case.
That trail still needs an expiration policy. Logging everything forever is not a safer default; it is a decision made by omission. Every record kept past its useful life is also a record that can leak, get subpoenaed, or go stale enough to mislead the next person who reads it.
Split the record types before picking a number
A single AI-assisted decision usually produces several distinct records, and they do not share a reason to exist. Treat each type on its own terms rather than assigning one retention period to the whole case file.
Operational records, the ones a team actually reopens, tend to have the shortest useful life. Compliance and dispute-defense records, the ones nobody expects to need until a customer complaint or a regulator asks a question, justify a longer hold, but only for the fields that answer that specific question.
- Source evidence pulled at decision time (documents, account state, ticket contents)
- The exact prompt, rule set, or model version applied to that case
- The model's output and any confidence or uncertainty signal
- The reviewer's decision, timestamp, and identity
- Personal data embedded in any of the above
Personal data inside the evidence has its own clock
When the evidence a workflow captures includes personal data, the storage-limitation principle in Article 5 of the GDPR applies whether or not the business operates in the EU: identifying data should be kept only as long as the purpose it was collected for requires. That purpose, not administrative convenience, is what should set the clock.
In practice this means separating the decision record from the personal data inside it wherever possible. A workflow that can summarize an approval and drop the underlying account number after the dispute window closes keeps its audit value while shrinking what a future breach could expose.
Write deletion into the workflow, not onto a calendar reminder
A retention policy that lives in a document and depends on someone remembering to run a cleanup script will not survive contact with a busy quarter. The deletion or redaction step belongs in the same system that captured the record: a scheduled job against the evidence store, a lifecycle rule on the object storage bucket, or a database policy tied to the case's closed date.
NIST's AI Risk Management Framework treats this kind of lifecycle control as part of managing AI risk, not as a separate compliance exercise bolted on afterward. Building the expiration in at design time is cheaper than retrofitting it once the evidence store has years of undifferentiated records in it.
Review the schedule when the workflow's stakes change
A retention schedule set for a low-stakes classification task will be wrong once the same workflow starts feeding a decision with financial, medical, or employment consequences. Higher-stakes decisions usually justify longer dispute-defense retention on the narrow fields that support an appeal, alongside shorter retention on everything else.
Revisit the schedule whenever the workflow's permissions, write-back actions, or audience change. The right retention period is a property of what the decision does to a person, not a fixed setting chosen once at launch.