Antitrust legal analyses

Identifies documents containing references to antitrust legal analyses in international contexts. These are commercially and legally sensitive competition analyses; the category is not personally identifiable information.

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

8

Platform compatibility

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

Pattern

(?is)\b(?:antitrust\s+legal\s+analyses|antitrust\s+analysis|competition\s+law|market\s+dominance|price\s+fixing|merger\s+clearance|market\s+concentration|restraint\s+of\s+trade|competition\s+review)\b

Corroborative evidence keywords

antitrust legal analyses, antitrust, legal, analyses, corporate, governance

Proximity: 300 characters

Should match

• antitrust legal analyses — Primary topic phrase match
• antitrust analysis — Case-insensitive topic phrase match
• competition law — Alternative topic phrase match
• market dominance — 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
• board policy — Generic word pair from old broad template should not match

Known false positives

• Common words and phrases related to antitrust legal analyses 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.accc.gov.au/business/competition-and-exemptions/competition-and-exemptions
• https://www.accc.gov.au/business/competition-and-exemptions/cartels