Cod Numeric Personal
Detects Cod Numeric Personal 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, ro
- 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[1-8]\d{12}\bCorroborative evidence keywords
CNP, cod numeric personal, personal code, ID number, identification, ID card, license, permit, registration, certificate, data record, database record, record set, data extract, data export, database table, spreadsheet, data registry, registry entry, master data (+13 more)
Proximity: 300 characters
Should match
1750101123456— Romanian CNP (male, born 1975)2880512567890— Romanian CNP (female, born 1988)5920303456789— Romanian CNP (male, born 1992 - foreign)
Should not match
0750101123456— Invalid first digit (0)9750101123456— Invalid first digit (9)template example placeholder record identifier— Template/sample context should be excluded even when anchor words are present
Known false positives
- The first-digit constraint (1-8) and 13-digit length provide good structural validation, but long numeric strings may appear in other contexts. Mitigation: Validate embedded date (digits 2-7) and require corroborative evidence keywords for higher confidence.
- 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.