Post-merger integration plans

Identifies documents containing references to post-merger integration plans 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(?:post[\s-]+merger\s+integration|PMI\s+plan|integration\s+workstream|day\s+one\s+readiness|transition\s+plan|integration\s+milestone|cultural\s+integration|systems\s+migration|organizational\s+design|integration\s+office|change\s+management)\b

Corroborative evidence keywords

post-merger integration plans, post, merger, integration, plans, mergers, acquisitions, strategy, acquisition, takeover, due diligence, scheme of arrangement, material non-public information, MNPI, inside information, price sensitive, price sensitive information, field, column, row (+26 more)

Proximity: 300 characters

Should match

• post-merger integration — Primary topic phrase match
• pmi plan — Case-insensitive topic phrase match
• integration workstream — Alternative topic phrase match
• day one readiness — 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
• merger fundraising — Generic word pair from old broad template should not match

Known false positives

• Common words and phrases related to post-merger integration plans 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/mergers
• https://www.takeovers.gov.au/guidance-notes