Item detail
github.com

microsoft/presidio

microsoft/presidio is a developer tool in RepoRadar's Radar section, holding Silver tier and a 'try now' verdict. Its strongest signal is workflow potential, scored 8.9 out of 10.

Score7.8
Popularity6.6
Riskconditional
TierSilver
Score breakdown
Usefulness8.0
Novelty6.0
Momentum7.0
Maturity5.8
Open-source/build8.4
Evidence7.2
Workflow potential8.9
Setup ease6.4

Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.

Why it matters

Useful for AI builders, security teams, and data engineers who want a self-hostable, Microsoft-maintained PII detection and redaction layer they can drop into text and image pipelines before data reaches an LLM, a vector store, or an analytics dashboard.

Who should use it

AI builders who need to scrub PII before sending text to LLMs or storing it in vector stores security and privacy teams that want a self-hostable PII detection layer with pluggable recognizers data engineers who want to anonymize sensitive fields before they hit analytics or logging systems Microsoft / Azure-adjacent teams that want a Microsoft-maintained PII SDK with familiar tooling

Who should skip it

Consider microsoft/presidio lower priority if you already have a working solution in this category.

About this signal

microsoft/presidio is tracked by RepoRadar as a developer tool in the Radar section. First seen 2026-06-21; the source record was last checked on 2026-06-21. The current verdict is 'try now' with a Silver tier and moderate setup difficulty. microsoft/presidio leads on workflow potential (8.9) and open-source/build quality (8.4); its lowest signal is maturity (5.8), so factor that in before investing setup time. This page summarizes the public evidence on the linked source page and states where additional review is still needed.

How this item is evaluated

The microsoft/presidio record combines a 7.8/10 composite score with separate popularity (6.6), risk (conditional), and setup (moderate) signals. See the scoring methodology for the current weights and evidence definitions.

Putting this into practice? Read How to evaluate an AI tool before you adopt it for the checklist behind this score.

Risk explanation

It processes text and images that may contain regulated PII (names, IDs, financial / health data), so confirm it runs only inside a controlled environment, that redaction outputs are stored securely, and that the team has a legal review of detection recall before treating presidio as a privacy control of record; It is a Microsoft-maintained SDK that ships with example analyzers and is designed to be extended, so pluggable recognizers should be reviewed for false positives and false negatives before they are trusted in production.

Evidence links
Closest alternatives / related signals
pii privacy redaction de-identification security open-source microsoft
Verification record

What RepoRadar actually verified

Tested in a bounded workflow

Bounded representative workflow retained by RepoRadar verification harness. Last checked 2026-07-13T10:35:38.683037Z.

passed · cohort-20260712-presidio-custom-identifier-scan

Tester
RepoRadar automated local verification harness
Started
2026-07-13T10:35:30.622642Z
Completed
2026-07-13T10:35:38.683037Z
Environment
Windows 10 AMD64; Python 3.11.9; credential-stripped child environment; disposable home/cache
Install/setup time
1 minute(s)
Evidence scope
Bounded representative workflow
Cleanup
Per-check temporary home and work directory removed. Shared cohort package cache removed.
Actions exercised
  • Created a disposable home, work directory, and isolated package cache with credential-like environment variables excluded.
  • Defined a scored TKT-###### pattern for a custom INTERNAL_TICKET_ID entity.
  • Scanned synthetic operational text, asserted exactly two matches and their spans, and retained the entity type, count, and scores.
  • Executed bounded check: Configure a Presidio PatternRecognizer and detect two custom ticket identifiers in disposable text.
  • Captured the complete sanitized stdout, stderr, exit status, artifact checks, and 8.06-second wall time.
Observed results
  • Command exited 0 after 8.06 seconds.
  • Presidio found both synthetic ticket identifiers, ignored the nonmatching order number, and returned the configured 0.85 score for each match.
  • Expected marker 'CHECK_OK entity=INTERNAL_TICKET_ID matches=2 scores=0.85,0.85' was observed in retained output.
  • Validated result.json: 4 required marker(s) present and 1 excluded marker(s) absent; size and SHA-256 are retained.
Observed strengths
  • The recognizer API supported a domain-specific identifier with explicit entity naming, regex behavior, spans, and confidence scores entirely locally.
Friction
  • A custom recognizer requires maintaining the pattern and threshold; this fixture cannot estimate false positives or recall on real operational text.
  • Setup or runtime emitted 9 stderr line(s); the complete warnings/errors are preserved in the retained log.
Limitations
  • The custom regex recognizer tests deterministic analyzer primitives only; it does not cover the AnalyzerEngine NLP stack, multilingual models, context enhancement, anonymization, false-positive tuning, or unstructured production documents.
  • This credential-free disposable workflow does not establish production scale, model quality, reliability under sustained use, or team adoption.

Pricing assessment: The custom Presidio analyzer primitive ran from the local open-source package with no hosted privacy service or model fee.

Privacy assessment: Only synthetic identifiers were scanned in memory; no NLP model, telemetry endpoint, cloud analyzer, or external data transfer was used.

Open retained test log →

Verification sources

Longitudinal intelligence

How this decision record is moving

Raw history JSON →

44 dated snapshots retained from 2026-06-21 through 2026-08-13; see the snapshot index for explicit coverage gaps. Stars, version, release, pricing, integration, risk, maintenance, verdict, score, and momentum fields remain explicit even when a source has not reported them. Repository momentum is a normalized 0–10 RepoRadar signal; GitHub stars appear only where the popularity monitor retained exact timestamped observations.

RepoRadar score7.8 current · +0.0 net
Repository momentum9.6 current · +2.6 net
GitHub stars (observed)10,470 current · +481 net
GitHub stars10,470 exact observation
Version2.2.364
Last release2026-07-22T08:30:12Z
Maintenanceactive
Current riskconditional
Current verdicttry now
Pricing baselineNo structured commercial pricing baseline
Pricing checkedNot applicable or not recorded
Pricing freshnessNo dated commercial pricing review
Integrations baselineNo structured integrations recorded

Recent dated points

DateScoreMomentumStarsRiskVerdictMaintenance
2026-08-137.89.610,470conditionaltry nowactive
2026-08-127.89.610,459conditionaltry nowactive
2026-08-117.89.610,432conditionaltry nowactive
2026-08-107.89.610,409conditionaltry nowactive
2026-08-097.89.310,396conditionaltry nowactive
2026-08-087.89.610,390conditionaltry nowactive
2026-08-077.89.610,320conditionaltry nowactive
2026-08-067.87.0Not recordedconditionaltry nownot recorded
2026-08-057.87.0Not recordedconditionaltry nownot recorded
2026-08-047.89.610,320conditionaltry nowactive
2026-08-037.89.610,320conditionaltry nowactive
2026-08-027.89.310,301conditionaltry nowactive

Why the record changed

stars changed

Stars changed: 10459 → 10470.

stars changed

Stars changed: 10432 → 10459.

stars changed

Stars changed: 10409 → 10432.

stars changed

Stars changed: 10396 → 10409.

stars changed

Stars changed: 10390 → 10396.

stars changed

Stars changed: 10320 → 10390.

stars changed

Stars changed: 10301 → 10320.

stars changed

Stars changed: 10294 → 10301.

stars changed

Source-observed stars changed: 10289 → 10294. This reports the retained observation delta and does not infer why the upstream change occurred.

stars changed

Source-observed stars changed: 10288 → 10289. This reports the retained observation delta and does not infer why the upstream change occurred.

stars changed

Stars changed: 10214 → 10245.

stars changed

Source-observed stars changed: 10213 → 10214. This reports the retained observation delta and does not infer why the upstream change occurred.