Aadhaar
Detects Aadhaar 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
- boost_regex
- 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
- in
- Regulations
- DPDPA, IT Act 2000 (India)
- 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[2-9]\d{3}\s?\d{4}\s?\d{4}\bCorroborative evidence keywords
identifier, number, ID, ID number, identification, ID card, license, permit, registration, certificate, field, column, row, entry, record, value, form, register, database, extract (+16 more)
Proximity: 300 characters
Should match
2345 6789 0123— Spaced Aadhaar number234567890123— Continuous Aadhaar number9876 5432 1098— High-range Aadhaar
Should not match
1234 5678 9012— Starts with 1 (must start with 2-9)0234 5678 9012— Starts with 0 (must start with 2-9)2345 6789 012— Only 11 digits instead of 12template 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 aadhaar 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 Hindi and English (India), 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.