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.
💡 Why It Matters
Presidio addresses the critical need for protecting sensitive data, particularly personally identifiable information (PII), in various environments. It is especially beneficial for ML/AI teams tasked with developing applications that require data privacy compliance. As a production-ready solution, Presidio has reached a mature state, making it suitable for integration into existing workflows. However, it may not be the right choice for projects requiring real-time processing or those with highly specialised data types that demand tailored solutions.
🎯 When to Use
This is a strong choice when teams need a reliable open source tool for engineering teams focused on data anonymization and privacy in applications. Teams should consider alternatives when they require more advanced features or specific compliance measures that Presidio does not support.
👥 Team Fit & Use Cases
Data scientists, machine learning engineers, and software developers typically use Presidio to ensure data privacy in their applications. It is commonly integrated into products and systems that handle sensitive information, such as data analytics platforms and customer relationship management systems.
🎭 Best For
🏷️ Topics & Ecosystem
📊 Activity
Latest commit: 2026-02-13. Over the past 75 days, this repository gained 705 stars (+11.3% growth). Activity data is based on daily RepoPi snapshots of the GitHub repository.