Cross-border transfer assessments

Name: Cross-border transfer assessments
Creator: TestPattern Community
License: https://opensource.org/licenses/MIT

Identifies documents containing references to cross-border transfer assessments 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: pii
Scope: wide
Platform compatibility: Purview: Compatible, GCP DLP: Compatible, Macie: Compatible, Zscaler: Compatible, Palo Alto: Degraded, Netskope: Unsupported

Pattern

(?is)\b(?:cross[\s-]+border\s+transfer|transfer\s+impact\s+assessment|adequacy\s+decision|binding\s+corporate\s+rules|standard\s+contractual\s+clauses|third\s+country\s+transfer|data\s+export|transfer\s+mechanism|supplementary\s+measures|privacy\s+compliance|overseas\s+disclosure|international\s+transfer)\b

Corroborative evidence keywords

cross-border transfer assessments, cross, border, transfer, assessments, privacy, compliance, risk

Proximity: 300 characters

Should match

cross-border transfer — Primary topic phrase match
transfer impact assessment — Case-insensitive topic phrase match
adequacy decision — Alternative topic phrase match
binding corporate rules — 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
data inventory compliance — Generic word pair from old broad template should not match

Known false positives

Common words and phrases related to cross-border transfer assessments 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://www.oaic.gov.au/privacy/australian-privacy-principles-guidelines
https://www.oaic.gov.au/privacy/privacy-guidance-for-organisations-and-government-agencies/privacy-impact-assessments