Privacy and preparation
Is Microsoft Presidio enough to redact PII before sharing data?
By SourceX Editorial · Updated
Short answer
Presidio is not enough on its own to redact PII before sharing data. It is a capable first layer for patterned identifiers, but its own documentation says there is no guarantee it will find all sensitive information. Use it as one stage, with custom recognizers, context lists, a second model, human sample review and a residual-rate test before release.
Key takeaways
- Presidio is an open-source, MIT-licensed SDK that combines named-entity recognition, regular expressions, rules and checksums to find PII in text and images.
- Presidio is now an independent, community-governed project under the Data Privacy Stack organization rather than a Microsoft-owned one.
- Default settings miss context-dependent identifiers and flag internal codes and product names, so tune it on your own records.
- No detector, open source or commercial, should be the release gate; a residual-rate test on the final output should be.
What Presidio is, and who maintains it now#
Presidio is an open-source, MIT-licensed SDK for identifying and anonymizing PII in text and images. It combines named-entity recognition, regular expressions, rule-based logic and checksums with surrounding context, and it includes a module for redacting PII in images.
The project began at Microsoft, which is why most searches still call it Microsoft Presidio. It has since moved to an independent, community-governed open-source project under the GitHub organization Data Privacy Stack, a change recorded in release 2.2.363, dated June 28, 2026, and it remains MIT-licensed. Teams running it in containers should update their image references, which moved with the project.
The verdict: a first layer, not a release gate#
Presidio is a strong first layer and a poor release gate. Its documentation is direct about this: because it uses automated detection, there is no guarantee it will find all sensitive information, and additional systems and protections should be employed.
The distinction matters for sign-off. A first layer reduces the volume of identifiers that people must deal with; a release gate is the evidence someone relies on to say the dataset is safe to share. Presidio does the first job well and was not designed for the second.
| Use Presidio for | Do not rely on Presidio alone for |
|---|---|
| Finding emails, phone numbers, card numbers and other patterned identifiers at scale | Deciding that a dataset is safe to release |
| Running inside your own environment, so text stays on your network | Catching people described only by context |
| Adding custom recognizers for your own ID formats | Names written in lowercase, as nicknames or as surnames alone |
| Applying consistent replacement operators across systems | Screenshots and scanned files without separate review |
| A repeatable, versioned redaction step | Proving residual risk to a licensee or to counsel |
Where Presidio falls short on business records#
Presidio falls short on business records mainly where identification depends on context or on vocabulary its default models have not seen. Support tickets, Jira comments and Slack threads are full of both.
Misses cluster around person names that are lowercase, abbreviated or also ordinary words, usernames and handles, partial addresses, and descriptions that identify someone without naming them. False positives cluster around product names, customer company names that look like surnames, and digit strings such as ticket, order or build numbers that resemble phone or ID numbers.
Both problems are configuration problems as much as model problems. Score thresholds, the choice of language model and the set of enabled recognizers all shift the balance, so a benchmark run on someone else's configuration says little about yours.
Five add-ons that make a Presidio pipeline release-ready#
Five additions turn Presidio from a scanner into a pipeline you can defend to counsel and to a licensee. Each one closes a known gap rather than adding tools for their own sake.
Wire the five into a single pipeline driven by one configuration file under version control. The same settings then run on every source system, re-runs are reproducible, and the release record can cite one configuration version instead of a set of notebooks and manual fixes.
- Custom recognizers for your formats: customer account IDs, job numbers, internal usernames and any ID that embeds a person's name or initials.
- Context and dictionary lists: employee rosters, CRM contacts and vendor names as deny lists, with product, module and location names on an allow list.
- A second recognition model run alongside the default, with a written rule for reconciling disagreements.
- Human review of a risk-ranked queue and of a random sample of everything Presidio passed.
- A residual-rate test: label a fresh sample of the final output and count what is left before anyone approves release.
Presidio vs commercial PII tools#
Presidio and commercial PII tools differ more in operation than in principle. All of them detect entities and transform them, and none removes the need to test on your own records.
Google's API also documents de-identification transforms such as deterministic encryption and date shifting, which help when records must stay linkable without exposing raw identifiers. Whichever tool you choose, record its version and configuration with each release.
| Tool | What it is | Documented capability | What you still need |
|---|---|---|---|
| Presidio | Open-source SDK, self-hosted | Text and image redaction; recognizers combine NER, patterns, rules and checksums | Tuning, a second model, human review, residual testing |
| Google Sensitive Data Protection | Managed inspection and de-identification service | Works on text, images and Google Cloud storage; risk analysis with k-anonymity, l-diversity, k-map and delta-presence | Review of sending data to the service; the same testing on your records |
| Amazon Comprehend PII detection | Managed language service | Per its developer guide as archived in June 2023, 22 universal PII entity types, with redaction run as an asynchronous batch job | A check of current entity types and limits; the same testing |
| LLM-based detection | Prompted general-purpose model | Often better with context | Consistency checks, data handling review, cost control |
Illustrative: a software company builds a pipeline around Presidio#
Illustrative: a fictional accounts payable automation vendor wants to license Jira issues, code review comments and support tickets that trace how invoice-matching bugs were found and fixed. The CTO's team starts with Presidio on default settings and labels a sample before trusting the output.
The labeled sample shows two problems. Presidio flags a parsing module named after a founder's dog as a person across many comments, and it misses customer staff referred to by lowercase first names in support replies. Tenant IDs that embed a customer's company name pass untouched.
The team adds a tenant ID recognizer, loads CRM contacts as a deny list and module names as an allow list, runs a second recognition model, and routes support threads with low-confidence detections to two reviewers. Slack direct messages are excluded. A residual test on a fresh sample of the output comes back clean against the standard set with counsel, and the release goes to the CTO for sign-off.
How SourceX treats tools like Presidio#
SourceX treats any detector, open source or commercial, as one stage in Preparation within the SourceX five-step transaction. What matters for release is the measured result on the supplier's own records, not the name of the tool.
The privacy record in the SourceX Evidence Packet lists the tools and versions used, the configuration, the review queue rules and the residual test results. The supplier approves that record before anything moves to Delivery.
Frequently asked questions
Does Presidio handle screenshots and images?
It includes an image redactor that uses OCR to find text in images and covers the matching regions. That helps with screenshots of printed text, but OCR misses handwriting, low-resolution text and anything that is not text, such as faces. Review kept images by hand.
Is Presidio free to use commercially?
Presidio is released under the MIT license, which permits commercial use. Check the licenses of any recognition models or language packages you plug into it, since those come from other projects with their own terms.
Can Presidio replace values instead of deleting them?
Yes. Its anonymizer applies operators such as replacing, masking or hashing detected values. Typed placeholders, such as a person or location tag, usually keep records more useful than deletion while still removing the identifier.
Does the move away from Microsoft change anything for users?
The license stays MIT and the code continues under the Data Privacy Stack organization. The practical changes are new repository and container image locations, so update dependencies and deployment scripts, and follow the new project's releases for security fixes.
How does Presidio handle records in languages other than English?
Presidio relies on an underlying language model for each language you configure, and many recognizers depend on context words that are written in English. Configure and test each language separately, add context words in that language, and exclude records in languages you cannot test properly.
Should we train our own detection model instead?
Rarely as a first step. Custom recognizers, dictionaries and a second off-the-shelf model often close the largest gaps in business records with far less effort. Consider a custom model only after labeled testing shows a persistent miss pattern that configuration cannot fix.
Sources
- Presidio is an open-source, MIT-licensed SDK for PII identification and anonymization in text and images. It combines named-entity recognition, regular expressions, rule-based logic and checksums with context, and includes a module that redacts PII in images. Source
- Presidio's documentation warns that because it is using automated detection mechanisms, there is no guarantee that Presidio will find all sensitive information, and that additional systems and protections should be employed. Source
- Presidio moved from a Microsoft-owned project to an independent, community-governed open-source project under the GitHub organization Data Privacy Stack, recorded in release 2.2.363 dated 2026-06-28; it remains MIT-licensed and container images moved to ghcr.io/data-privacy-stack. Source
- Google's Sensitive Data Protection works on text, images and Google Cloud storage repositories, offers k-anonymity, l-diversity, k-map and delta-presence risk metrics, and supports transforms including deterministic encryption and date shifting. Source
- As documented in the Amazon Comprehend Developer Guide archived on GitHub in June 2023, Comprehend detects 22 universal PII entity types, and redaction runs as an asynchronous batch job. Source
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