Genetic data

Identifies documents containing references to genetic data 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

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

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

(?is)\b(?:genetic\s+data|genetic\s+testing|DNA\s+sample|genomic\s+data|genetic\s+marker|genetic\s+profile|hereditary\s+information|genetic\s+predisposition|chromosomal\s+analysis|gene\s+sequencing|genetic\s+counseling)\b

Corroborative evidence keywords

genetic data, genetic, data, personal, identity, demographics, neurodata, brain-computer interface, neural recording, neural data, brain scan data, EEG data, brain imaging, biometric, biometrics, biometric data, biometric information, biometric template, biometric identifier, field (+28 more)

Proximity: 300 characters

Should match

• genetic data — Primary topic phrase match
• genetic testing — Case-insensitive topic phrase match
• DNA sample — Alternative topic phrase match
• genomic data — 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 genetic data 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