社会保険番号
Detects 社会保険番号 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. 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
- jp
- Regulations
- APPI
- 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{4}-\d{6}-\d{1}\b
Corroborative evidence keywords
社会保険, social insurance, SIN, 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
1234-567890-1— Japanese SIN with dashes9876-543210-9— Another SIN format4567-890123-4— Valid SIN number
Should not match
1234-56789-1— Too few digits in middle group1234-567890-12— Too many digits in last groupsample 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
- The specific dash-separated format (XXXX-XXXXXX-X) reduces false positives considerably. Mitigation: The structured format provides good inherent validation. Keyword context further 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.