contextjet-ai/awesome-llm-observability/scrub-pii-from-text

Scrub PII from text

Observability captures prompts and completions, which in a regulated app can mean shipping account numbers or PII to a third-party backend. This skill ships a scrubber you can run in the export path so raw values never leave your process.

Use the bundled script

`scripts/pii_scrub.py` is pure Python, no install needed:

from pii_scrub import scrub, find_pii
scrub("email a@b.com, card 4111 1111 1111 1111")   # -> "email <email>, card <card>"
find_pii(text)                                      # [("email", "a@b.com"), ("card", ...)]

It masks emails, credit cards (Luhn-validated, so random 16-digit order ids are not flagged), US SSNs, phone numbers, IPv4 addresses, and IBANs. Run it directly: python scripts/pii_scrub.py.

How to apply it

1. Redact before export, not after: call scrub() in your span processor / logging hook so no path bypasses it (see redact-pii-for-tracing for where that hook goes).
2. Redact both directions: user input and model output (models echo PII back).
3. Layer a real detector for high stakes: this is a strong default, but for finance/health, add Presidio plus a domain ruleset. Regex alone misses context-dependent PII.

Validation

Run the tests: pytest skills/scrub-pii-from-text/tests/. They confirm emails/SSNs/valid cards are masked, that a Luhn-invalid number is left alone (false-positive guard), that clean text is untouched, and that find_pii reports entities.

Anti-patterns

• Regex-only redaction as your whole compliance story for high-stakes data.
• Masking input but not model output.
• Redacting after the SDK already exported the span (mask before export).