Rail signaling configurations
Identifies documents containing references to rail signaling configurations 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
- structural regex with domain-specific anchors and constrained context replaces phrase-only marker. 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(?:rail\s+signaling|signal\s+configuration|block\s+section|train\s+control|track\s+circuit|points\s+machine|route\s+setting|aspect\s+sequence|positive\s+train\s+control|wayside\s+signal)\b
Corroborative evidence keywords
rail signaling configurations, rail, signaling, configurations, critical, infrastructure, systems
Proximity: 300 characters
Should match
rail signaling— Primary topic phrase matchsignal configuration— Case-insensitive topic phrase matchblock section— Alternative topic phrase matchtrain control— Additional topic phrase match
Should not match
unrelated generic text without domain phrases— No relevant topic phrases presentplaceholder value 12345— Random text should not match topic-specific regexrail signal configuration— Generic word pair from old broad template should not match
Known false positives
- Common words and phrases related to rail signaling configurations 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.legislation.gov.au/C2018A00029/latest/text
- https://www.cyber.gov.au/resources-business-and-government/essential-cyber-security/ism