在留カード番号
Detects 在留カード番号 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. 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[A-Z]{2}\d{8}[A-Z]{2}\b
Corroborative evidence keywords
在留カード, residence card, zairyū card, 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
AB12345678CD— Residence card with letter-digit-letter formatXY98765432EF— Another residence card numberMN45678901GH— Valid residence card format
Should not match
A123456789CD— Only one leading letterAB12345678C— Only one trailing lettertemplate example placeholder record identifier— Template/sample context should be excluded even when anchor words are present
Known false positives
- The distinctive format (XX99999999XX) is relatively specific but may match some product or serial codes. 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.