I would start by asking what was collected for what purpose. A trace can contain user text, retrieved document spans, tool arguments, model outputs, identifiers, embeddings, screenshots, and an external effect. Deleting obvious names from a JSON field does not remove a unique contract clause or a private code fragment. The risk is also not limited to public model training. An evaluator, annotator, vendor, or another tenant's developer may see an example in a shared dashboard. The NIST definition of minimization limits processing and retention to what is necessary for an authorized purpose. That is the design question, with the customer's actual terms determining what is permitted.

Keep the production trace store and the learning data pipeline separate. For debugging, retain a short lived, access controlled trace with enough identifiers to reconstruct a failure. For a shared regression set, prefer authored or synthetic cases validated against the real failure mode, or transform an approved production case into a reviewed artifact with the smallest necessary facts. A detector that removes email addresses is one control in that process, not the release gate. Record origin tenant, source object revisions, permitted use, location, transformations, reviewers, retention deadline, and every derived dataset version. Restrict raw samples to the tenant's boundary until a purpose specific authorization allows export. An opt-in to evaluate service quality is not automatically an opt-in to fine-tune a model or send data to an external judge. OpenAI's external model eval documentation, for instance, explicitly warns that such calls pass data to third parties. The product and contract details matter more than the label “eval.”

For deletion, search lineage rather than a single trace table. Identify raw traces, exported rows, annotations, cached prompts, evaluation outputs, backups under their retention policy, and training files or checkpoints if training was allowed. Stop future use at once, revoke access to copies, delete where supported, and record what remains and why under the applicable agreement. If a model has already been trained on the data, removing a row from a dataset does not magically remove its influence from existing weights. That requires a separately defined remediation and customer disclosure process. Do not promise exact unlearning without evidence.

The counterargument is that real traces are the best way to catch real failures. I agree on the value. I would keep a small, governed, tenant scoped holdout for approved uses and invest in transformations that preserve the failure mechanism. Test a redacted sample for reconstructable secrets, rare strings, linkable metadata, and permissions before broadening access. Track eval quality too, because a scrubbed example that no longer represents the bug is a poor test. Both privacy and measurement fail if we call a crude replace operation “anonymization.”