Critical spare parts inventories
Identifies documents containing references to critical spare parts inventories 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(?:critical\s+spare\s+parts|spares?\s+inventory|lead\s+time)\b
Corroborative evidence keywords
critical spare parts inventories, critical, spare, parts, inventories, infrastructure, systems
Proximity: 300 characters
Should match
Critical spare parts inventories— Exact phrase marker matchcritical spare parts inventories— Case-insensitive phrase matchCritical spare parts inventories— Normalized whitespace phrasestructured sample with matching anchors— Structural anchor sample
Should not match
unrelated generic text— No relevant phrase contextplaceholder value 12345— Random text should not match phrase markergeneric policy prose without anchors— Should reject generic mentions without structural anchor termstemplate example placeholder record identifier— Template/sample context should be excluded even when anchor words are present
Known false positives
- Common words and phrases related to critical spare parts inventories 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