Critical spare parts inventories

Identifies documents containing references to critical spare parts inventories in Australian 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.

Detection quality

Mixed

Jurisdictions

global

Regulations

AML/CTF Act (Cth), HRIPA (Cth), IPA 2009 (Qld), My Health Records Act 2012 (Cth), NDB Scheme (Cth), Privacy Act 1988 (Cth), TIA Act 1979 (Cth)

Frameworks

ISO 27001, ISO 27701

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 match
• critical spare parts inventories — Case-insensitive phrase match
• Critical spare parts inventories — Normalized whitespace phrase
• structured sample with matching anchors — Structural anchor sample

Should not match

• unrelated generic text — No relevant phrase context
• placeholder value 12345 — Random text should not match phrase marker
• generic policy prose without anchors — Should reject generic mentions without structural anchor terms

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 Australian English, 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