Asmens kodas
Detects Asmens kodas 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. Context label evidence plus explicit template/example exclusion improves precision for high-risk identifiers. Added context gating and exclusion rules improve precision and reduce incidental matches.
- Detection quality
- Verified
- Jurisdictions
- eu, lt
- 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{11}\bCorroborative evidence keywords
asmens kodas, personal code, identity number, 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
38801011234— Lithuanian personal code (male, 1988)49203035678— Lithuanian personal code (female, 1992)37501014567— Lithuanian personal code (male, 1975)
Should not match
1234567890— Too few digits (10)123456789012— Too many digits (12)sample template placeholder number 123456789— Template/sample context should be excluded even when numeric-like values appeartemplate example placeholder record identifier— Template/sample context should be excluded even when anchor words are present
Known false positives
- Eleven-digit numeric sequences may match phone numbers or other administrative identifiers. Mitigation: Require corroborative evidence keywords such as "asmens kodas" within the proximity window.
- 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.