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[LT-03] The False Positive Problem: Why Pure ML...

[LT-03] A 2024 benchmark found Presidio generated 13,536 false positive name detections across 4,434 samples — flagging pronouns, vessel names...

March 23, 20268 min skaityti
Presidio false positive ratePII detection precisionautomated redaction costlegal document reviewhybrid PII detection

[LT-03]

The 22.7% Precision Problem in Production

A 2024 benchmark study of Microsoft Presidio — the open-source PII detection engine used in legal technology, healthcare, and enterprise data protection applications — found a 22.7% precision rate for person name detection in business document contexts.

Precision measures the accuracy of positive identifications: what percentage of the items the tool flagged as "person names" are actually person names. At 22.7%, approximately **77 out of every ...

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