data-privacy-stack/presidio

data-privacy-stack/presidio has 11,754 GitHub stars and 1,416 forks. GitHot measured +24 stars in 24 hours. Last checked 2026-11-06 10:40 UTC. Last push 2026-11-02.

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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.

11,754 étoiles 1,416 forks +24 in 24h

What you can do

Predefined or custom PII recognizers leveraging Named Entity Recognition, regular expressions, rule based logic and checksum with relevant context in multiple languages.
Options for connecting to external PII detection models.
Multiple usage options, from Python or PySpark workloads through Docker to Kubernetes.
Customizability in PII identification and de-identification.

Progression mesurée des étoiles

in 24h: +24
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in 1 month: Historique en cours

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README.md

Original

Presidio - Data Protection and De-identification SDK

:mega: Presidio has moved to a new home! Read more here :mega:

Context aware, pluggable and customizable PII de-identification service for text and images.

Release

Component	Downloads	Coverage
Presidio Analyzer
Presidio Anonymizer
Presidio Image-Redactor
Presidio Structured

What is Presidio

Presidio (Origin from Latin praesidium ‘protection, garrison’) helps to ensure sensitive data is properly managed and governed. It provides fast identification and anonymization modules for private entities in text such as credit card numbers, names, locations, social security numbers, bitcoin wallets, US phone numbers, financial data and more.

Presidio demo gif

:blue_book: Full documentation

:mega: Project transition update

:question: Frequently Asked Questions

:thought_balloon: Demo

:flight_departure: Examples

Goals

Allow organizations to preserve privacy in a simpler way by democratizing de-identification technologies and introducing transparency in decisions.
Embrace extensibility and customizability to a specific business need.
Facilitate both fully automated and semi-automated PII de-identification flows on multiple platforms.

Main features

Predefined or custom PII recognizers leveraging Named Entity Recognition, regular expressions, rule based logic and checksum with relevant context in multiple languages.
Options for connecting to external PII detection models.
Multiple usage options, from Python or PySpark workloads through Docker to Kubernetes.
Customizability in PII identification and de-identification.
Module for redacting PII text in images (standard image types and DICOM medical images).

:warning: Presidio can help identify sensitive/PII data in un/structured text. However, because it is using automated detection mechanisms, there is no guarantee that Presidio will find all sensitive information. Consequently, additional systems and protections should be employed.

Installing Presidio

Running Presidio

Support

Before you submit an issue, please go over the documentation.
For general discussions, please use the GitHub repo's discussion board.
If you have a usage question, found a bug or have a suggestion for improvement, please file a GitHub issue.
For other matters, please email [email protected].

Contributing

For details on contributing to this repository, see the contributing guide.

This project has adopted the Contributor Covenant Code of Conduct.