Amazon Macie
Managed sensitive data discovery and protection for Amazon S3.
Presidio
Context‑aware, extensible SDK for detecting and redacting PII
Presidio provides a pluggable framework to identify, mask, and anonymize personally identifiable information in text, images, and structured data, supporting custom recognizers, multiple languages, and deployment via Python, Docker, or Kubernetes.
Overview
Overview
Presidio is designed for organizations that need to protect privacy while processing large volumes of data. It offers a modular pipeline that can detect, mask, and anonymize PII across unstructured text, image files (including DICOM), and structured datasets.
Capabilities
The framework includes an Analyzer for entity detection, an Anonymizer for flexible redaction strategies, and an Image‑Redactor for visual data. Users can employ predefined recognizers based on NER, regex, rule‑based logic, or checksum, and they can also plug in external models or create custom recognizers to meet domain‑specific needs. Deployment options span from simple Python scripts to PySpark workloads, Docker containers, and Kubernetes clusters, enabling both automated and semi‑automated privacy workflows.
Extensibility
Presidio’s architecture encourages customization at every stage—recognizer selection, masking technique, and post‑processing logic—so teams can align the tool with regulatory requirements such as GDPR or HIPAA while maintaining transparency in decision making.
Highlights
Predefined and custom recognizers using NER, regex, rules, and checksum
Pluggable pipeline that can integrate external detection models
Supports text, image (including DICOM), and structured data de‑identification
Deployable via Python, PySpark, Docker, or Kubernetes
Pros
- Highly customizable to fit specific compliance needs
- Multi‑language support for global data sets
- Unified handling of text and image PII
- Flexible deployment across on‑prem and cloud environments
Considerations
- Requires configuration and tuning for optimal accuracy
- No guarantee of 100 % PII detection; supplemental controls needed
- Performance depends on chosen models and hardware
- Learning curve for building custom recognizer pipelines
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Fit guide
Great for
- Enterprises building data pipelines that must comply with privacy regulations
- Healthcare applications needing to redact patient identifiers in images
- Developers seeking an open‑source alternative for PII masking
- Teams that require extensible, language‑agnostic privacy tooling
Not ideal when
- Ultra‑low‑latency streaming where detection overhead is unacceptable
- Simple one‑off scripts that only need basic regex replacement
- Environments without Python or container support
- Projects demanding certified, zero‑risk privacy guarantees
How teams use it
Automated data sanitization for analytics
Redacts personal identifiers from logs and datasets before they are ingested into analytics platforms.
Redacting patient identifiers in DICOM images
Removes visible and embedded PII from medical imaging files while preserving diagnostic content.
PII masking in customer support transcripts
Ensures chat and email logs are anonymized before storage or model training.
Custom recognizer for domain‑specific identifiers
Detects proprietary codes or serial numbers unique to a business using rule‑based logic.
Tech snapshot
Python100%
Dockerfile1%
HTML1%
Shell1%
Frequently asked questions
Which languages does Presidio support for PII detection?
Presidio includes recognizers for multiple languages and can be extended with language‑specific models or regex patterns.
How can I add a custom recognizer?
Create a recognizer class implementing the required interface, register it in the pipeline configuration, and optionally supply custom regex or rule definitions.
Can Presidio run on Kubernetes?
Yes, the SDK can be containerized and deployed as a microservice on Kubernetes, scaling with your workload.
Does Presidio guarantee 100 % detection of all PII?
No. Automated detection may miss some sensitive data, so additional safeguards should be used alongside Presidio.
What image formats are supported for redaction?
Standard image types and DICOM medical images are supported out of the box.
Project at a glance
Active- Stars
- 6,700
- Watchers
- 6,700
- Forks
- 909
LicenseMIT
Repo age7 years old
Last commit3 days ago
Primary languagePython
Last synced 4 hours ago