Italy Value Added Tax Number
Detects Italy Value Added Tax Number patterns. This pattern is based on a Microsoft Purview built-in sensitive information type. VAT numbers have country-specific prefixes that aid detection accuracy.
- Type
- regex
- Engine
- universal
- Confidence
- high
- Confidence justification
- High confidence: the IT prefix followed by exactly 11 digits is a distinctive format for Italian VAT (partita IVA) numbers. Added context gating and exclusion rules improve precision and reduce incidental matches.
- Detection quality
- Verified
- Jurisdictions
- it, eu
- Regulations
- GDPR
- Data categories
- financial, business-identifier
- Scope
- narrow
- Risk rating
- 5
- Platform compatibility
- Purview: Compatible, GCP DLP: Compatible, Macie: Compatible, Zscaler: Compatible, Palo Alto: Compatible, Netskope: Compatible
Pattern
\bIT\d{11}\b
Corroborative evidence keywords
partita IVA, IVA, VAT, imposta sul valore aggiunto, codice fiscale, value added tax, VAT number, belasting, BTW, Mehrwertsteuer, TVA, Umsatzsteuer, VAT registration, BTW-nummer, numéro de TVA, Steuernummer, tax number, tax registration, taxe sur la valeur ajoutée, adószám (+44 more)
Proximity: 300 characters
Should match
IT12345678901— Standard Italian VAT number formatIT98765432101— Italian partita IVA with IT prefixIT11223344556— Italian VAT identification number
Should not match
IT1234567890— Only 10 digits after IT prefix, too shortFR12345678901— French prefix, not Italian VAT formattemplate example placeholder record identifier— Template/sample context should be excluded even when anchor words are present
Known false positives
- Other identifier schemes that coincidentally share a similar prefix and digit structure Mitigation: Validate the complete format including prefix and digit count. Layer with document context to confirm financial or tax-related content.
- Test or example VAT numbers used in documentation or training materials Mitigation: Maintain an allow-list of known test/example numbers. Use document classification to distinguish production data from training content.