Draft:Microsoft Presidio

• Comment: In accordance with Wikipedia's Conflict of interest policy, I disclose that I have a conflict of interest regarding the subject of this article. Commonpipes (talk) 15:21, 6 April 2025 (UTC)

---

Presidio is an open-source software (OSS) project developed by Microsoft, designed to detect and de-identify personally identifiable information (PII) in text, structured, and semi-structured data. It is widely used to support data privacy, compliance, and safe data handling in production and machine learning environments.

Overview

Presidio enables the detection and de-identification of sensitive data such as names, phone numbers, credit card numbers, and social security numbers across a variety of data formats. It combines rule-based detection with natural language processing (NLP) techniques and supports custom recognizers, language models, and de-identification strategies.

The core components of Presidio include:

• Presidio Analyzer – detects PII entities in text using named entity recognition (NER), regex matching, and context-aware scoring.
• Presidio Anonymizer – replaces or redacts detected entities using methods such as masking, encryption, or hashing.
• Presidio Image Redactor – applies OCR to detect and de-identify PII in images.
• Presidio Structured – supports scalable de-identification of structured and semi-structured data such as JSON, CSV, and nested records.

Presidio is written in Python and offers REST APIs for easy integration with other tools and services.

Use Cases

Presidio is commonly used in a variety of privacy-sensitive workflows, including:

• LLM Input/Output Filtering – to detect and remove PII before sending prompts to large language models, or to de-identify model outputs that may include sensitive data.

• Log Scrubbing – to detect and trace PII in production logs and system traces.
• Dataset Preparation for ML – to de-identify training datasets prior to use in machine learning workflows.
• Data Sharing with Third Parties – as part of human-in-the-loop de-identification processes, where Presidio performs the initial automated PII removal and a human reviewer verifies completeness.

Use in the Open Source Ecosystem

Presidio is integrated into a growing number of open-source AI and NLP frameworks:

• LangChain – used to preprocess user inputs and LLM outputs to ensure privacy and compliance.
• Rasa – includes Presidio-based middleware for PII management in chatbot pipelines.
• LlamaIndex – uses Presidio to de-identify documents before they are indexed in retrieval-augmented generation (RAG) pipelines.
• Open-metadata - Auto-classification of sensitive data for data governance.

These integrations position Presidio as a foundational component in privacy-aware generative AI systems.

Guardrails for Generative AI

Presidio is used as a guardrail to mitigate privacy risks in generative AI applications. It helps enforce content safety policies by analyzing prompts and outputs for sensitive content and applying de-identification as needed.

• Nemo Guardrails uses Presidio to detect and control sensitive content in LLM interactions.
• Guardrails AI use Presidio to implement PII detection and filtering within customizable validation guards for LLM outputs.

Responsible AI

Presidio supports responsible AI development by helping teams uphold privacy principles such as data minimization and informed consent. Its configurable detection and de-identification pipeline allows users to tailor the tool for regulatory compliance (e.g., GDPR, HIPAA, CCPA) and ethical AI practices.

Customizability

Presidio is designed to be highly customizable to meet specific needs. Users can embed new types of detection modules, create custom PII recognizers, and adapt the tool to various datasets, file types, languages, and use cases. This flexibility allows organizations to tailor Presidio to their unique privacy and compliance requirements.

Licensing and Availability

Presidio is released under the MIT license and is actively maintained on [https.com/microsoft/presidio GitHub].