Guide · Open source
How to use Presidio: detect and redact PII in Python
Updated · written with AI from the project's own documentation and checked against our catalogue data
Presidio is an open-source Python framework for finding sensitive personal information (PII) and then redacting, masking or anonymizing it. This guide is for developers and data engineers who want to remove personal details from data before it is stored, shared or processed. After reading it, you will know what Presidio does, how to get the source code, and what to test before you rely on it.
Presidiodata-privacy-stack/presidio · ★ 11.6k · MIT Install options & downloads Quick start
- 1Confirm Python is available. Presidio's listed prerequisite is Python. Check that Python is installed on the machine where you will run the framework.
- 2Clone the repository. Run the git clone command from the README to download the Presidio source code to your machine.
- 3Read the repository documentation. The README excerpt used here is limited, so use the repository docs to find the installation and API instructions.
- 4Test on sample data. Run detection and redaction on a small set of your own examples and record what is caught and missed.
What Presidio does
Presidio is a framework for detecting sensitive personal information and then transforming it. The project describes its scope as text, images and structured data, and it combines NLP, pattern matching and customizable pipelines.
People typically use it to remove personal details from data before storing, sharing or processing it.
- Detect PII in text, images and structured data
- Redact, mask or anonymize what it finds
- Combine NLP, pattern matching and custom pipelines
Check whether Presidio fits your use case
Presidio is a Python framework, not a point-and-click application. The README excerpt used for this guide does not describe a graphical app, so plan on writing code.
The source also does not document accuracy figures. Treat detection results as something to measure on your own data, not as a guarantee.
- Good fit: developers adding PII detection to Python data pipelines
- Good fit: teams de-identifying text before sending it to other services
- Good fit: privacy-focused teams that want a self-hosted, inspectable MIT-licensed tool
- Poor fit: anyone who needs a no-code tool or guaranteed detection
Get the Presidio source code
The README excerpt gives a clone command for the repository. Run it in the directory where you want the project to live. The project's homepage is presidio.dataprivacystack.org.
Python is the prerequisite listed for the project. The source excerpt does not give further setup details, so check the repository for installation instructions beyond the clone step.
git clone https://github.com/data-privacy-stack/presidioRun your first detection and redaction
Once the code is available, the basic workflow is to pass text to Presidio, detect PII entities, and then apply a redaction, masking or anonymization step. Start with a small sample of text that contains obvious personal details so you can see what is caught and what is missed.
The exact API calls are not included in the README excerpt used here, so use the repository documentation for the specific functions and parameters.
- Start with a small sample that includes names, emails and phone numbers
- Review both caught and missed entities
- Keep the original data separate from the output
Test results on your own data before relying on them
Because accuracy is not documented in the source, build a small labeled test set from your own data. Record what Presidio finds, what it misses and what it flags incorrectly.
Repeat the test whenever you change recognizers, pipeline settings or the kind of text you process.
- Label a sample of real or realistic records by hand
- Measure missed detections as well as false alarms
- Retest after each configuration change
What people use it for
- Removing names, emails and phone numbers from text before it is sent to an external service
- Preparing datasets that must not expose personal details before sharing them
- Adding a PII redaction step to a Python data pipeline before storage
Frequently asked questions
Is Presidio free to use?
Presidio is released under the MIT license, which the project lists. The source does not give further terms, so read the license file in the repository to confirm what it allows for your project.
What do I need to run Presidio?
The listed prerequisite is Python. The README excerpt used for this guide does not give other requirements, so check the repository for details.
How do I install Presidio?
The README excerpt shows a git clone command for the repository. The source does not include further installation commands in the excerpt, so follow the repository's installation instructions after cloning.
Is Presidio the same as Microsoft Presidio?
Yes, according to the project notes used for this guide, Microsoft Presidio is the same project. The repository is data-privacy-stack/presidio.
What are alternatives to Presidio?
Other PII tools mentioned for comparison include scrubadub, spaCy-based redaction tools and Google Cloud Data Loss Prevention. Google Cloud DLP is commercial and not open source, while the others are alternatives you can self-host.
Does Presidio detect PII with 100% accuracy?
No guarantee is given. The README excerpt does not document accuracy figures, so test Presidio on your own data to measure how well it detects and misses personal information.
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Our verdict
Presidio suits Python developers and data teams who want a self-hosted, inspectable MIT-licensed framework for PII detection and redaction. It is a poor choice if you need a no-code tool or guaranteed detection, and you should test it on your own data before depending on it.
Install options & downloads