# data-privacy-stack/presidio

An open-source framework for detecting, redacting, masking, and anonymizing sensitive data (PII) across text, images, and structured data. Supports NLP, pattern matching, and customizable pipelines.

Repository: https://github.com/data-privacy-stack/presidio
Canonical: https://ross.abutalabs.com/products/presidio
Homepage: https://presidio.dataprivacystack.org
Language: Python
License: MIT
License Family: permissive
Topics: python, pii, privacy, data-anonymization, de-identification, data-masking, transformers, pii-detection, data-obfuscation, data-privacy, data-redaction, guardrails, image-redactor, named-entity-recognition, nlp, personally-identifiable-information, phi, sensitive-data, spacy, anonymization
Last push: 2026-08-26T17:35:40+00:00

## Health v2 (maintenance only)
Score: 93/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 82, longevity 100
- inputs: {"age_days": 3043, "days_push": 7, "days_rel": 42, "gap_med": 60.5, "n_releases_24m": 9}
- flags: no_readme
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 10644, forks 1261 (observed 2026-08-28T04:10:43.623080+00:00)

## What it is
Presidio is an open-source Python SDK for detecting, redacting, masking, and anonymizing sensitive data (PII) in text, images, and structured/semi-structured data. It combines NLP-based named entity recognition, regex, rule-based logic, and checksum validation in customizable pipelines, with modules for analysis, anonymization, image redaction, and structured data.

## Use cases
- redact PII from customer support transcripts
- anonymize sensitive data in text before sharing with third parties
- mask credit card numbers and SSNs in logs
- remove personally identifiable information from images
- de-identify PHI in healthcare records
- detect PII in structured/tabular datasets
- build GDPR/CCPA data privacy compliance pipelines

## When to choose
- you need customizable, self-hosted PII detection and anonymization in Python
- you want to combine NLP models (spaCy, transformers, stanza) with rule-based recognizers
- you need to redact PII from text, images, and structured data with one toolkit
- you want a library/SDK you can embed in pipelines rather than a managed cloud service

## When to avoid
- you need a guaranteed complete detection of all sensitive data - automated detection is not exhaustive
- you want a fully managed PII detection service without customization effort
- you need languages or entity types not covered by available recognizers without building custom ones

## Facets
- artifact type: library
- maturity: active
- function: nlp, security, privacy, ocr, machine-learning, sdk
- domain: privacy, security, data-science, developer-tools
- platform: python, cross-platform, cli
- tags: pii-detection, data-anonymization, de-identification, data-masking, ner, spacy, transformers, image-redaction, gdpr-compliance, phi, natural-language-processing, docker, kubernetes

## Member repositories
- data-privacy-stack/presidio (main) score 93

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:43.623080+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:18:14.152144+00:00, confidence not recorded.
  - homepage: https://presidio.dataprivacystack.org (fetched 2026-08-29T08:17:24.833584+00:00, sha a7721f700b30)
  - site_page: https://presidio.dataprivacystack.org/installation (fetched 2026-08-29T08:17:24.842945+00:00, sha b85e83b7f97c)
  - registry_pypi: https://pypi.org/pypi/presidio/json (fetched 2026-08-29T08:17:24.847107+00:00, sha c1eea7a735be)
  - site_page: https://presidio.dataprivacystack.org/faq (fetched 2026-08-29T08:17:24.845138+00:00, sha 22e9a17f7bda)
- Data as of 2026-08-30T08:39:29.467469+00:00.