Au Number Plates
Detects Australian number plate patterns. Standard plate formats only; personalised plates excluded. Requires vehicle/registration keyword context.
- Type
- regex
- Engine
- universal
- Confidence
- low
- Confidence justification
- Low confidence: short alphanumeric plate formats can easily match product codes, abbreviations, and other non-vehicle identifiers. Corroborative evidence keywords such as registration, rego, or number plate are essential for reliable detection.
- Detection quality
- Partial
- Jurisdictions
- au
- Regulations
- AML/CTF Act (Cth), HRIPA (Cth), IPA 2009 (Qld), My Health Records Act 2012 (Cth), NDB Scheme (Cth), Privacy Act 1988 (Cth), TIA Act 1979 (Cth)
- Frameworks
- ISO 27001, ISO 27701
- Data categories
- location, pii
- Scope
- wide
- Risk rating
- 5
- Platform compatibility
- Purview: Compatible, GCP DLP: Compatible, Macie: Compatible, Zscaler: Compatible, Palo Alto: Compatible, Netskope: Compatible
Pattern
\b[A-Z]{2}[\s\-]?\d{2}[\s\-]?[A-Z]{2}\bCorroborative evidence keywords
registration, rego, number plate, address, age, birthday, citizenship, city, date of birth, DOB, email, ethnicity, fax, first name, full name, gender, given name, last name, maiden name, middle name (+12 more)
Proximity: 300 characters
Should match
AB12CD— NSW standard plate format (AA99AA)XY99ZZ— NSW plate upper rangeDL45KM— Standard NSW format
Should not match
ABC123— Three letters followed by three digits (not a valid format)A1B2C3— Alternating letters and digits (not a valid format)1234AB— Four digits followed by two letters (not a valid format)
Known false positives
- Common words and phrases related to au number plates 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 Australian English, 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.
- High-frequency pattern matches in large document corpora due to broad regex anchors. Expected match rate is significantly higher than specific identifier patterns. Mitigation: Tune confidence thresholds for bulk scanning. Consider using this pattern primarily as a pre-filter with secondary validation.