RRN
Detects RRN 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
- kr
- Regulations
- PIPA (South Korea)
- 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}-?[1-8]\d{6}\bCorroborative evidence keywords
identifier, number, ID, 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
900101-1234567— South Korean RRN with hyphen9001011234567— RRN without hyphen850315-2345678— Female RRN (digit 2)
Should not match
900101-0234567— Gender digit 0 (must be 1-8)900101-9234567— Gender digit 9 (must be 1-8)90010-1234567— Only 5 digits in date section instead of 6template 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 rrn 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 Korean, 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.