Firearm license number

Identifies documents containing references to firearm license number 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

(?is)\b(?:firearm\s+license\s+number)\b\s{0,80}\b[A-Z0-9][A-Z0-9\-/ ]{4,24}\b

Corroborative evidence keywords

firearm license number, drivers licence, driver licence, drivers license, driver license, DL, licence number, license number, ID, identifier, number, reference, code, index, serial, account, file number, case number, record number, ref (+17 more)

Proximity: 240 characters

Should match

structured record with identifier and contextual anchors — Structural anchor sample

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 firearm license number 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.