Quick answerCan the classification of an existing data type change later? Yes, and it should be reviewed whenever the data's actual use changes materially, for example if a category originally kept for product analytics starts getting used for a new purpose that changes its risk profile. Does a low-volume data type still need this process, or only high-volume ones? Volume does not change the governance obligation; a rarely-produced but highly sensitive data type needs the same explicit classification as a high-volume one, arguably more urgently given the risk concentration.
Quick answer
Assign a single named owner, not a committee, who must classify any new AI agent data category within a fixed window (30 days is a reasonable default) of it first appearing, using three questions in order: does a regulation or contract require keeping it, does it carry ongoing business or product value that justifies the storage and risk of keeping it, and does keeping it expose more re-identifiable customer detail than the value justifies. The default when none of the three clearly says keep is purge, not archive, since an unreviewed default of keeping everything is how retention schedules quietly grow into a liability nobody remembers approving.
This is a governance question, not the scheduling mechanics
Building a scheduled retention and purge process for AI agent conversation data covers how to run the schedule once categories already exist: automated purge jobs, per-category clocks, a legal-hold exception flag. That post assumes raw transcripts, structured account-action records, and aggregated analytics are already defined buckets. It does not answer what happens the day your agent's tooling produces a genuinely new kind of data, a voice-print snippet from a new channel, a structured output from a new tool integration, an embedding vector nobody classified before, and nobody has yet decided which bucket, if any, it belongs in. Without an explicit governance process for that moment, new data types default to whichever engineer built the feature guessing, which is how retention schedules end up full of undocumented exceptions.
Why a fixed classification deadline matters
A new data type without an assigned classification tends to sit in an unmanaged holding pattern indefinitely, since no engineering team wants to be the one who deletes something that might turn out to matter, and no engineering team is incentivized to spend time classifying data that is not blocking their current sprint. A fixed deadline (30 days from first production appearance is a reasonable default) forces the decision to actually happen instead of accumulating as permanent, unreviewed exceptions to your stated retention policy.
The three-question test, applied
Start with whether a specific regulation, contract clause, or active litigation hold already requires retention; if yes, the classification is settled and the data joins the archive bucket regardless of the other two questions. If no external requirement applies, ask whether the data has clear, ongoing product or business value, not hypothetical future value, that justifies the storage cost and re-identification risk of keeping it. If that answer is also no, the default is purge on the same routine schedule as its closest existing category, not an indefinite unclassified holding pattern.
What this does not cover
This governance process decides the everyday default for new data categories. It is a different question from a specific customer's subpoenaed disclosure request or a one-off deletion request that runs into a shared-model-behavior complication; those are exception paths that can override the default classification for a specific record or customer, not the standing rule for how a new data type gets classified in the first place.