MBI
'Detects MBI patterns. Excluded letters: S, L, O, I, B, Z.' 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: structurally constrained pattern with corroborative keyword support reduces false positive rates significantly.
- Detection quality
- Verified
- Jurisdictions
- us
- Regulations
- CCPA/CPRA, FTC Act s5, HIPAA, State Breach Laws (US)
- Frameworks
- ISO 27001, ISO 27701, SOC 2
- Data categories
- phi, healthcare
- Scope
- specific
- Risk rating
- 8
- Platform compatibility
- Purview: Compatible, GCP DLP: Compatible, Macie: Compatible, Zscaler: Compatible, Palo Alto: Compatible, Netskope: Compatible
Pattern
\b[1-9][AC-HJKMNP-RT-Y][0-9AC-HJKMNP-RT-Y]\d[AC-HJKMNP-RT-Y][0-9AC-HJKMNP-RT-Y]\d[AC-HJKMNP-RT-Y]{2}\d{2}\b
Corroborative evidence keywords
MRN, medical record number, patient ID, NPI, DEA, medicare, medicaid, insurance ID, member ID, beneficiary, ICD-10, ICD-9, CPT, NDC, SNOMED, HCPCS, diagnosis code, procedure code, drug code
Proximity: 300 characters
Should match
1AC4DE7HJ90— Standard MBI format9YT2FG8KP12— Alternate MBI3HK5MN7PR34— Another valid MBI
Should not match
0AC4DE7HJ90— Starts with 0 (must start with 1-9)1BC4DE7HJ90— Invalid second character (B not in valid set)1AC4DE7HJ9— Only 10 characters instead of 11
Known false positives
- Medical terminology in health education materials, research publications, clinical guidelines, or public health documents without patient-specific data. Mitigation: Require corroborative evidence keywords confirming patient context. Look for co-occurrence with patient identifiers such as medical record numbers or dates of birth.
- General wellness and fitness content using medical vocabulary without constituting protected health information. Mitigation: Layer with patient identifier patterns or healthcare-specific document structure detection to distinguish clinical records from general health content.