{"data":{"slug":"data-privacy-stack-presidio","name":"presidio","tagline":"A framework for detecting and anonymizing sensitive data","github_url":"https://github.com/data-privacy-stack/presidio","owner":"data-privacy-stack","repo":"presidio","owner_avatar_url":"https://avatars.githubusercontent.com/u/275623515?v=4","primary_language":"Python","stars":10818,"forks":1279,"topics":["anonymization","data-anonymization","data-masking","data-obfuscation","data-privacy","data-redaction","de-identification","guardrails","image-redactor","named-entity-recognition","nlp","personally-identifiable-information","phi","pii","pii-detection","privacy","python","sensitive-data","spacy","transformers"],"archived":false,"github_pushed_at":"2026-09-10T15:20:43+00:00","maintenance_label":"Very active","stars_delta_30d":423,"url":"https://www.graphcanon.com/tools/data-privacy-stack-presidio","markdown_url":"https://www.graphcanon.com/tools/data-privacy-stack-presidio.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/data-privacy-stack-presidio","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=data-privacy-stack-presidio","description":"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.","homepage_url":"https://presidio.dataprivacystack.org","license":"MIT","open_issues":111,"watchers":100,"ai_summary":"Presidio is an open-source, context-aware, pluggable, and customizable PII de-identification service that supports text, images, and structured data anonymization using NLP, pattern matching, and custom pipelines.","readme_excerpt":"# Presidio - Data Protection and De-identification SDK\n## :mega: Presidio is moving to a new home! [Read more here](docs/project_transition.md) :mega:\n**Context aware, pluggable and customizable PII de-identification service for text and images.**\n\n---\n\n\n\n\n\n\n\n\n| Component | Downloads | Coverage |\n|-----------|-----------|----------|\n| Presidio Analyzer |  |  |\n| Presidio Anonymizer |  |  |\n| Presidio Image-Redactor |  |  |\n| Presidio Structured |  |  |\n## What is Presidio\n\nPresidio _(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.\n\n\n\n---\n\n### :blue_book: [Full documentation](https://data-privacy-stack.github.io/presidio)\n\n### :mega: [Project transition update](docs/project_transition.md)\n\n### :question: [Frequently Asked Questions](docs/faq.md)\n\n### :thought_balloon: [Demo](https://huggingface.co/spaces/presidio/presidio_demo)\n\n### :flight_departure: [Examples](https://data-privacy-stack.github.io/presidio/samples/)\n\n---\n\n\n### Goals\n\n- Allow organizations to preserve privacy in a simpler way by democratizing de-identification technologies and introducing transparency in decisions.\n- Embrace extensibility and customizability to a specific business need.\n- Facilitate both fully automated and semi-automated PII de-identification flows on multiple platforms.\n\n### Main features\n\n1. **Predefined** or **custom PII recognizers** leveraging _Named Entity Recognition_, _regular expressions_, _rule based logic_ and _checksum_ with relevant context in multiple languages.\n2. Options for connecting to external PII detection models.\n3. Multiple usage options, **from Python or PySpark workloads through Docker to Kubernetes**.\n4. **Customizability** in PII identification and de-identification.\n5. Module for **redacting PII text in images** (standard image types and DICOM medical images).\n\n: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.\n\n## Installing Presidio\n\n1. [Using pip](https://data-privacy-stack.github.io/presidio/installation/#using-pip)\n2. [Using Docker](https://data-privacy-stack.github.io/presidio/installation/#using-docker)\n3. [From source](https://data-privacy-stack.github.io/presidio/installation/#install-from-source)\n4. [Migrating from V1 to V2](./docs/presidio_V2.md)\n\n## Running Presidio\n\n1. [Getting started](https://data-privacy-stack.github.io/presidio/getting_started)\n2. [Setting up a development environment](https://data-privacy-stack.github.io/presidio/development)\n3. [PII de-identification in text](https://data-privacy-stack.github.io/presidio/text_anonymization)\n4. [PII de-identification in images](https://data-privacy-stack.github.io/presidio/image-redactor)\n5. [Usage samples and example deployments](https://data-privacy-stack.github.io/presidio/samples)\n\n---\n\n## Support\n\n- Before you submit an issue, please go over the [documentation](https://data-privacy-stack.github.io/presidio/).\n- For general discussions, please use the [GitHub repo's discussion board](https://github.com/data-privacy-stack/presidio/discussions).\n- If you have a usage question, found a bug or have a suggestion for improvement, please file a [GitHub issue](https://github.com/data-privacy-stack/presidio/issues).\n- For other matters, please email [presidio@dataprivacystack.org](mailto:presidio@dataprivacystack.org).\n\n## Contributing\n\nFor details on contributing to this repository, see the [contributing guide](CONTRIBUTING.md).\n\nThis project has adopted the [Contributor Covenant Code of Conduct](CODE_OF_CONDUCT.md).\n\n## Contribu","github_created_at":"2018-05-04T11:08:58+00:00","created_at":"2026-07-15T10:42:02.234586+00:00","updated_at":"2026-09-20T04:25:40.77517+00:00","categories":[{"slug":"data-retrieval","name":"Data & Retrieval","url":"https://www.graphcanon.com/categories/data-retrieval","markdown_url":"https://www.graphcanon.com/categories/data-retrieval.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/data-retrieval"},{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"}],"tags":[{"slug":"data-anonymization","name":"data-anonymization"},{"slug":"data-obfuscation","name":"data-obfuscation"},{"slug":"group-python-frameworks","name":"group:python-frameworks"}],"trust":{"provenance":{"is_fork":false,"github_id":132129752,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-11T06:00:24.746Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":2,"days_since_push":0,"last_release_at":"2026-07-22T08:30:12Z","stars_delta_30d":423,"open_issues_delta_30d":9},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-15T10:42:03.696Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-11T06:00:25.219Z"},"deploy":{"source":"dockerfile:docker-compose.yml","self_host":true,"observed_at":"2026-09-11T06:00:25.219Z","managed_saas":false},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-09-11T06:00:25.219Z"},"has_docker":{"value":true,"source":"dockerfile:docker-compose.yml","observed_at":"2026-09-11T06:00:25.219Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-09-11T06:00:25.219Z"}},"decision_facts":{"hosting":null,"pricing":{"model":"freemium","summary":"Open-source and freely usable as it relies on the MIT license; however, additional support services may incur costs."},"requirements":{"min_ram_gb":null,"requires_docker":true},"constraints":{"min_ram_gb":null,"pricing_model":"freemium","requires_docker":true},"when_to_use":["When you need a tool that supports not only text but also image and structured data anonymization, Presidio offers broad coverage for different data types.","If your project requires the use of Python or PySpark along with Docker or Kubernetes support, Presidio fits well due to its multiple compatibility options.","In contexts where customizable PII recognizers leveraging NER, regular expressions, rule-based logic and checksum are necessary, Presidio provides flexibility in recognizing specific entities."],"when_not_to_use":["Avoid using Presidio if your project strictly requires manual data anonymization processes as it mainly supports automated detection.","Presidio's automated mechanisms may not catch all sensitive information, so you should not solely rely on it when a near-perfect accuracy rate in PII identification is crucial."],"source":"enrich:decision_facts","observed_at":"2026-07-17T02:06:43.869Z"},"constraint_facets":{"min_ram_gb":null,"pricing_model":"freemium","requires_docker":true},"decision_summary":[{"label":"Pricing","value":"freemium - Open-source and freely usable as it relies on the MIT license; however, additional support services may incur costs."},{"label":"Requirements","value":"Requires Docker"},{"label":"Adopt for","value":"Presidio is an open-source framework for identifying and anonymizing sensitive data including text, images, and structured formats through its NLP, pattern matching, and customizable pipeline capabilities."},{"label":"License detail","value":"MIT License for use under permissive terms that allows free usage for commercial or non-commercial purposes with full source code available."}]}}