Voter roll extract files

Identifies documents containing references to voter roll extract files 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: pii
Scope: wide
Platform compatibility: Purview: Compatible, GCP DLP: Compatible, Macie: Compatible, Zscaler: Compatible, Palo Alto: Degraded, Netskope: Unsupported

Pattern

(?i)\b(?:voter\s+roll\s+extract\s+files?)\b

Corroborative evidence keywords

voter roll extract files, file number, reference number, case number, record number, account number, serial number, identification number, registration number, customer number, member number, reference code, ID number, data record, database record, record set, data extract, data export, database table, spreadsheet (+5 more)

Proximity: 240 characters

Should match

Voter roll extract files — Topic phrase present
voter roll extract files — Topic phrase, lowercase
this record references the voter roll extract files held on file — Topic phrase within surrounding text

Should not match

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 voter roll extract files 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.