MAC addresses linked to users

Identifies documents containing references to mac addresses linked to users 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

Mixed

Jurisdictions

au

Regulations

SOCI Act 2018 (Cth), TIA Act 1979 (Cth)

Frameworks

CIS Controls, DISP, ISO 27001, NIST CSF, SOC 2

Data categories

pii

Scope

wide

Platform compatibility

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

Pattern

(?is)\b(?:MAC\s+addresses\s+linked\s+to\s+users|MAC\s+address|hardware\s+address|physical\s+address|network\s+interface|WiFi\s+address|Bluetooth\s+address|device\s+fingerprint|network\s+identifier|ethernet\s+address|wireless\s+adapter)\b

Corroborative evidence keywords

mac addresses linked to users, mac, addresses, linked, users, contact, location, data, address, age, birthday, citizenship, city, date of birth, DOB, email, ethnicity, fax, first name, full name (+56 more)

Proximity: 300 characters

Should match

• MAC addresses linked to users — Primary topic phrase match
• mac address — Case-insensitive topic phrase match
• hardware address — Alternative topic phrase match
• physical address — 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
• address mac — Generic word pair from old broad template should not match

Known false positives

• Common words and phrases related to mac addresses linked to users 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
• https://www.service.nsw.gov.au/transaction/check-a-vehicle-registration
• https://www.gsma.com/get-involved/working-groups/terminal-steering-group/imei-database/
• https://www.itu.int/rec/T-REC-E.118.1/en