sec-pii

name	sec-pii
description	Detect and de-identify PII before sensitive data reaches an AI model, a log, or an external service. Triggers when an AI pipeline sends user data to an LLM, when handling names / emails / phone / national-id / credit card / IBAN / medical records / addresses, and on "anonymize", "redact", "mask PII", "sanitize before sending to the model", "GDPR / HIPAA", or "don't leak user data to the AI". Builds a redaction step with Microsoft Presidio (analyze → anonymize) — masking, redacting, hashing or encrypting PII, with built-in or custom recognizers, for text, images and DICOM.
allowed-tools	Read, Grep, Glob, Bash, Write, Edit

PII shield — anonymize before data reaches the model

The fastest way to leak user data is to pipe raw input straight into an LLM (or a log, or a third-party API). Put a de-identification step in front: detect PII, then mask / redact / hash / encrypt it before it leaves your trust boundary. Microsoft Presidio (open-source, MIT) is the standard tool for this.

The pattern

1. Find where raw data crosses a boundary — prompts, RAG documents, tool outputs, error logs, analytics — anything sent to an LLM or a third party.
2. Analyze — presidio-analyzer detects PII (NER + regex + checksums): PERSON, EMAIL_ADDRESS, PHONE_NUMBER, CREDIT_CARD, IBAN_CODE, US_SSN / national-id, LOCATION, MEDICAL_LICENSE, crypto wallets, dates, and more — across languages.
3. Anonymize — presidio-anonymizer applies an operator per entity:

   Operator	Effect	Reach for it when
   redact	removes it	you don't need it at all
   replace	a placeholder (<PERSON>)	the model needs the shape, not the value
   mask	partial (****-**-1234)	keep a recognizable tail
   hash	one-way hash	join / dedupe without the value
   encrypt	reversible with a key	you must restore it later (decrypt inside your boundary)

4. Re-identify only inside your boundary (encrypt → decrypt with your key) — never at the model.

Minimal example (Python)

from presidio_analyzer import AnalyzerEngine
from presidio_anonymizer import AnonymizerEngine

text = "My name is Sara, card 4095-2609-9393-4932, email [e-mail]"
found = AnalyzerEngine().analyze(text=text, language="en")
safe = AnonymizerEngine().anonymize(text=text, analyzer_results=found).text
# -> "My name is <PERSON>, card <CREDIT_CARD>, email <EMAIL_ADDRESS>"   → send `safe` to the model

Beyond the basics

• Custom recognizers — add your own entities by deny-list, regex, or a NER model (spaCy / HuggingFace transformers); or plug in an external PII model. Domain IDs (member numbers, MRNs) → a regex recognizer with context words.
• Images + medical — presidio-image-redactor redacts PII text in images and in DICOM medical scans.
• Scale & deploy — a Python library; run it in Docker / Kubernetes, or over PySpark for big batches. Put it as a gateway step in the pipeline, not an afterthought.
• Tune it — read the analyzer's confidence scores, add context words, allow-list false positives. For anything leaving your boundary, prioritize recall (don't miss PII) over precision.

Don't

Don't send raw PII to the model "just for the prototype", don't log it before redaction, and don't trust the model to redact its own input. Redaction happens before the boundary, deterministically — not in the prompt.

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Built on Microsoft Presidio (github.com/microsoft/presidio, MIT) — analyzer + anonymizer + image/DICOM redactor. Complements sec-secrets-crypto (which guards your secrets); this guards your users' PII. See docs/ECOSYSTEM.md.