Személyi szám
Detects Személyi szám 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, hu
- 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-\d{6}-\d{4}\bCorroborative evidence keywords
személyi szám, personal identification, személyazonosító, 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
1-750101-1234— Male born before 19002-880512-5678— Female born before 19003-920303-4567— Male born after 1900
Should not match
12-750101-1234— Two digits before first dash1-75010-1234— Too few digits in date parttemplate example placeholder record identifier— Template/sample context should be excluded even when anchor words are present
Known false positives
- The distinctive dash-separated format (X-XXXXXX-XXXX) significantly reduces false positives. Mitigation: The structured format provides good inherent validation. Additional keyword context 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.