Purchase history

Identifies documents containing references to purchase history 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(?:purchase\s+history|order\s+history|transaction\s+log|buying\s+pattern|shopping\s+cart|order\s+record|purchase\s+date|transaction\s+history|invoice\s+detail|payment\s+record|customer\s+order|receipt\s+data)\b

Corroborative evidence keywords

purchase history, purchase, history, customer, commercial, data

Proximity: 300 characters

Should match

• purchase history — Primary topic phrase match
• order history — Case-insensitive topic phrase match
• transaction log — Alternative topic phrase match
• buying pattern — 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
• customer invoice — Generic word pair from old broad template should not match

Known false positives

• Common words and phrases related to purchase history 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/your-privacy-rights/your-personal-information/what-is-personal-information
• https://www.oaic.gov.au/privacy/australian-privacy-principles-guidelines