Personnummer

Detects Personnummer 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: boost_regex
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, se
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: Compatible

Pattern

\b(19|20)?\d{6}[-+]?\d{4}\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

19900101-1234 — Full personnummer with century
900101-1234 — Short personnummer format
200101+1234 — Person over 100 years old (plus sign)

Should not match

21900101-1234 — Invalid century prefix (21 instead of 19 or 20)
900101-123 — Only 3 digits in last group instead of 4
900101-12345 — 5 digits in last group instead of 4
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 personnummer 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

Sweden national ID entity definition - Microsoft Learn