Voiceprints

Identifies documents containing references to voiceprints 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

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: Degraded, Netskope: Unsupported

Pattern

(?is)\b(?:voice\s+recognition|voice\s+biometric|voice\s+template|voice\s+authentication|speaker\s+recognition|voice\s+sample|voice\s+pattern|voice\s+ID|vocal\s+biometric)\b

Corroborative evidence keywords

voiceprints, personal, identity, demographics, voiceprint, voice recognition, voice template, iris template, iris scan, retina scan, retinal scan, eye scan, field, column, row, entry, record, value, form, register (+21 more)

Proximity: 300 characters

Should match

• voice recognition — Primary topic phrase match
• voice biometric — Case-insensitive topic phrase match
• voice template — Alternative topic phrase match
• voice authentication — 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 voiceprints 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