How should you respond if security approves fine-tuning...

Relying on regex stripping for fine-tuning security is fundamentally flawed. While regex can successfully eliminate formatted patterns like email addresses or social security numbers, it completely misses unstructured PII such as names, places, job titles, or context-specific identifiers. Furthermore, because fine-tuning data contains repeated entities (e.g., a high-volume customer appearing across dozens of support tickets), data repetition drastically multiplies model memorization and extraction vulnerability.

To safely improve model tone without exposing sensitive data, generate synthetic support transcripts derived from the original dataset and fine-tune exclusively on the synthetic set. Keep original customer transcripts stored strictly in external datastores where erasure requests can be performed reliably via index operations. Run an automated extraction probe prior to deployment to empirically verify that no real entity patterns are extractable from the model.