PII detection
Also called Personally Identifiable Information detection
Spotting the names, emails, and card numbers hiding in text so you can strip or protect them before they leak.
Think of it like
A mailroom clerk who blacks out home addresses on anything headed for a public bulletin board.
Example
A support-ticket pipeline auto-redacts a customer’s phone number and SSN before the transcript is used to fine-tune a model.
How it actually works
Detectors mix regexes for structured items (emails, credit cards) with named-entity recognition for the fuzzy ones (names, locations). The hard part is recall on messy real text and deciding whether to redact, tokenize, or refuse — over-redaction breaks usefulness, under-redaction leaks. It is a first line of defense, not a guarantee.
For product teams
It is what lets you use real user data safely and is often a hard requirement for privacy and compliance sign-off.
For engineers
Layer regex/validators for structured PII with an NER model for unstructured mentions, then route hits to redaction, tokenization, or block.
Related
- A concrete tactic for protecting privacy.
- Input sanitization — Feeds the broader input sanitization step.
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