Biometric identifiers

Identifies documents containing references to biometric identifiers in international contexts. This information type is classified as personally identifiable information under applicable data protection regulations.

Type

regex

Engine

boost_regex

Confidence

medium

Confidence justification

identifier/document-structure anchored regex with constrained context replaces phrase-only detection. Added context gating and exclusion rules improve precision and reduce incidental matches.

Detection quality

Mixed

Jurisdictions

global

Regulations

GDPR

Data categories

government-id, pii

Scope

wide

Platform compatibility

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

Pattern

(?i)\b(?:biometric\s+identifiers?)\b

Corroborative evidence keywords

biometric identifiers, biometric, identifiers, personal, identity, demographics, biometrics, biometric data, biometric information, biometric template, biometric identifier, file number, reference number, case number, record number, account number, serial number, identification number, registration number, customer number (+15 more)

Proximity: 300 characters

Should match

• Biometric identifiers — Topic phrase present
• biometric identifiers — Topic phrase, lowercase
• this record references the biometric identifiers held on file — Topic phrase within surrounding text

Should not match

• unrelated generic text — No relevant phrase context
• placeholder value 12345 — Random text should not match phrase marker
• generic narrative without identifier/document anchors — Should not match plain mention
• template example placeholder record identifier — Template/sample context should be excluded even when anchor words are present

Known false positives

• Common words and phrases related to biometric identifiers 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 English (as the primary international business language), 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://eur-lex.europa.eu/eli/reg/2016/679/oj
• https://www.oaic.gov.au/privacy/your-privacy-rights/your-personal-information/what-is-personal-information