Taxpayer identification number

Identifies documents containing references to taxpayer identification number in Australian contexts. This information type is classified as personally identifiable information under the Privacy Act 1988.

Type

regex

Engine

boost_regex

Confidence

low

Confidence justification

Low confidence marker: phrase-based artifact detection to bootstrap line-by-line coverage. Requires corroborative evidence and later hardening to high-confidence structural patterns.

Detection quality

Partial

Jurisdictions

global

Regulations

AML/CTF Act (Cth), IPA 2009 (Qld), NDB Scheme (Cth), Privacy Act 1988 (Cth)

Frameworks

ISO 27001

Data categories

government-id, pii

Scope

wide

Platform compatibility

Purview: Compatible, GCP DLP: Compatible, Macie: Compatible, Zscaler: Compatible, Palo Alto: Compatible, Netskope: Compatible

Pattern

\btaxpayer\s+identification\s+number\b

Corroborative evidence keywords

taxpayer identification number, taxpayer, identification, number, government, ids, civil, status, agency, department, ministry, public sector, civil service, welfare, social services, public administration, statutory authority, regulatory body, public servant, government program (+2 more)

Proximity: 300 characters

Should match

• Taxpayer identification number — Exact phrase marker match
• taxpayer identification number — Case-insensitive phrase match
• Taxpayer identification number — Normalized whitespace phrase

Should not match

• unrelated generic text — No relevant phrase context
• placeholder value 12345 — Random text should not match phrase marker

Known false positives

• Common words and phrases related to taxpayer identification number 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.

References

• https://www.passports.gov.au/apply-or-renew
• https://www.ato.gov.au/individuals-and-families/tax-file-number
• https://www.abr.business.gov.au/FAQ/ABNBasics
• https://immi.homeaffairs.gov.au/visas/already-have-a-visa/check-visa-details-and-conditions/overview
• https://www.servicesaustralia.gov.au/how-to-prove-your-identity-with-centrelink
• https://www.aec.gov.au/enrol/
• https://www.nsw.gov.au/family-and-relationships/births/birth-certificates
• https://www.afp.gov.au/our-services/national-police-checks