What should I use to detect PII leakage in LLM outputs?

To detect PII leakage in LLM outputs, use a combination of rules, classifiers, and redaction/verification tools rather than a single detector.

Good options

1. PII/PHI detection libraries

   • Microsoft Presidio — one of the most common open-source choices for detecting names, emails, phone numbers, addresses, SSNs, etc.
   • spaCy + custom NER — useful if you need domain-specific entities.
   • Microsoft Text Analytics / Azure AI Language — managed cloud option.
   • AWS Comprehend and Google Cloud DLP — strong managed DLP products.

2. Regex + heuristics

   • Great for structured PII:
     • emails
     • phone numbers
     • credit card numbers
     • SSNs / national IDs
     • account numbers
   • Fast and easy, but misses unstructured or context-dependent PII.

3. LLM-based PII classification

   • Use a small classifier or an LLM prompt to label outputs as containing PII.
   • Best as a second layer, not the only layer.

4. Embedding/search-based checks for memorization

   • If you’re worried about the model reproducing training data or user data, compare outputs against:
     • known sensitive datasets
     • hashes / fingerprints
     • approximate matching for near-duplicates

Practical recommendation

A solid setup is:

• Regex for obvious patterns
• Presidio for entity detection
• LLM/classifier for contextual PII
• Human review for high-risk cases

If you want one default choice

Use Microsoft Presidio as the base tool, then add custom rules for your domain.

Important note

“PII leakage” can mean:

• the model outputs explicit personal data,
• it reveals hidden prompt/data,
• or it reconstructs sensitive info indirectly.

If you tell me your stack, I can recommend a specific toolchain for:

• batch offline evaluation
• real-time moderation
• cloud-native DLP
• open-source only