Root-cause corrective action plans

Identifies documents containing references to root-cause corrective action 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(?:root\s+cause|corrective\s+action|preventive\s+action|root\s+cause\s+analysis|containment\s+action|effectiveness\s+check|quality\s+improvement)\b

Corroborative evidence keywords

root-cause corrective action plans, root, cause, corrective, action, plans, product, quality, safety, SCADA, PLC, DCS, HMI, Modbus, Modbus TCP, Modbus RTU, DNP3, OPC-UA, OPC Classic, IEC 61850 (+40 more)

Proximity: 300 characters

Should match

• root cause — Primary topic phrase match
• corrective action — Case-insensitive topic phrase match
• preventive action — Alternative topic phrase match
• root cause analysis — 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
• product failure — Generic word pair from old broad template should not match

Known false positives

• Common words and phrases related to root-cause corrective action 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.safeworkaustralia.gov.au/doc/model-code-practice-how-manage-work-health-and-safety-risks
• https://www.productsafety.gov.au/product-safety-laws