Citizenship status

Identifies documents containing references to citizenship status 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

Topic false positive

Jurisdictions

global

Regulations

GDPR

Data categories

pii

Scope

wide

Risk rating

3

Platform compatibility

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

Pattern

(?is)\b(?:citizenship\s+status|permanent\s+resident|visa\s+holder|residency\s+status|dual\s+citizen|citizen\s+application|nationality\s+status|right\s+of\s+abode)\b

Corroborative evidence keywords

citizenship status, address, age, birthday, citizenship, city, date of birth, [object Object], email, ethnicity, fax, first name, full name, gender, given name, last name, maiden name, middle name, mobile, name (+61 more)

Proximity: 240 characters

Should match

• citizenship status — Primary topic phrase match
• permanent resident — Case-insensitive topic phrase match
• visa holder — Alternative topic phrase match
• residency status — 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 citizenship status 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.