Henkilötunnus (HETU)
Detects Henkilötunnus (HETU) 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
- high
- Confidence justification
- High confidence: pattern has strong structural constraints (specific format, prefix, or character class restrictions) that significantly reduce false positive rates. Added context gating and exclusion rules improve precision and reduce incidental matches.
- Detection quality
- Verified
- Jurisdictions
- eu, fi
- 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\d{6}[-+A]\d{3}[A-Z0-9]\b
Corroborative evidence keywords
henkilötunnus, HETU, personal identity code, ID number, identification, ID card, license, permit, registration, certificate, field, column, row, entry, record, value, form, register, database, extract (+19 more)
Proximity: 300 characters
Should match
010175-123A— Finnish HETU born in 1900s120388+234B— Finnish HETU born in 1800s150392A456C— Finnish HETU born in 2000s
Should not match
010175X123A— Invalid century character010175-12A— Too few digits in individual numbertemplate example placeholder record identifier— Template/sample context should be excluded even when anchor words are present
Known false positives
- The structured format with century marker significantly reduces false positives, though test data or documentation may contain example patterns. Mitigation: The century marker (-, +, A) provides strong structural validation. Additional keyword context improves accuracy.
- In multiple 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.