NIR Insee

Detects NIR Insee patterns. This pattern is based on a Microsoft Purview built-in sensitive information type. Users already running Purview may prefer to enable the built-in SIT directly, or use this version as a starting point for customisation.

Type: regex
Engine: universal
Confidence: medium
Confidence justification: Medium confidence: pattern has structural constraints but corroborative keywords are recommended to reduce false positive rates. Added context gating and exclusion rules improve precision and reduce incidental matches.
Detection quality: Verified
Jurisdictions: eu, fr
Regulations: BDSG, CNIL / LIL, GDPR
Frameworks: ISO 27001, ISO 27701
Data categories: pii, government-id
Scope: narrow
Risk rating: 9
Platform compatibility: Purview: Compatible, GCP DLP: Compatible, Macie: Compatible, Zscaler: Compatible, Palo Alto: Compatible, Netskope: Unsupported

Pattern

\b[12]\d{2}(0[1-9]|1[0-2]|[2-4][0-9])\d{2}(?:\d{3}|2[AB])\d{3}\d{2}\b

Corroborative evidence keywords

identifier, number, ID, ID number, identification, ID card, license, permit, registration, certificate, field, column, row, entry, record, value, form, register, database, extract (+16 more)

Proximity: 300 characters

Should match

185057512345678 — Male born 1985 in department 75
293019912345612 — Female born 1993 in department 99
190017512345634 — Person born 1990 in department 75

Should not match

3850575123456 78 — Invalid gender digit (3 instead of 1 or 2)
1851375123456 78 — Invalid month (13)
185057512345 78 — Too few digits in commune/sequence section
template example placeholder record identifier — Template/sample context should be excluded even when anchor words are present

Known false positives

Common words and phrases related to nir insee appearing in policy documents, training materials, HR templates, or compliance guidelines without actual personal data. Mitigation: Require corroborative evidence keywords within the proximity window to confirm sensitive data context rather than general discussion.
In multiple EU languages, similar terminology used in formal or administrative contexts (education, professional documentation) that does not constitute sensitive data collection. Mitigation: Layer with additional contextual signals such as structured identifiers, form fields, or database column headers to distinguish sensitive records from general references.

References

France social security number (INSEE) entity definition - Microsoft Learn