Previous legal names and aliases

Identifies documents containing references to previous legal names and aliases in Australian contexts. This information type is classified as personally identifiable information under applicable data protection regulations.

Type

regex

Engine

boost_regex

Confidence

medium

Confidence justification

category-aware structural regex with anchor and context constraints replaces phrase-only detection. Added context gating and exclusion rules improve precision and reduce incidental matches.

Detection quality

Not detected

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)

Frameworks

ISO 27001, ISO 27701, PCI-DSS, SOC 2

Data categories

government-id, pii

Scope

wide

Platform compatibility

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

Pattern

(?is)\b(?:previous\s+name|former\s+name|maiden\s+name|also\s+known\s+as|name\s+change|deed\s+poll|previous\s+legal\s+name|former\s+surname|birth\s+name)\b

Corroborative evidence keywords

previous legal names and aliases, previous, legal, names, aliases, personal, identity, demographics, address, age, birthday, citizenship, city, date of birth, DOB, email, ethnicity, fax, first name, full name (+1710 more)

Proximity: 300 characters

Should match

• previous name — Primary topic phrase match
• former name — Case-insensitive topic phrase match
• maiden name — Alternative topic phrase match
• also known as — Additional topic phrase match

Should not match

• unrelated generic text without domain phrases — No relevant topic phrases present
• placeholder value 12345 — Random text should not match topic-specific regex
• name disability — Generic word pair from old broad template should not match

Known false positives

• Common words and phrases related to previous legal names and aliases 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.oaic.gov.au/privacy/your-privacy-rights/your-personal-information/what-is-personal-information
• https://www.oaic.gov.au/privacy/your-privacy-rights/your-personal-information/what-is-sensitive-information
• https://www.oaic.gov.au/privacy/australian-privacy-principles-guidelines