Data protection impact assessments

Identifies documents containing references to data protection impact assessments in Australian contexts. This information type is classified as personally identifiable information under the Privacy Act 1988.

Type

regex

Engine

boost_regex

Confidence

medium

Confidence justification

category-aware structural regex with anchor and context constraints replaces phrase-only detection.

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(?:data\s+protection\s+impact\s+assessments|impact\s+assessment|privacy\s+impact\s+assessment|risk\s+assessment|data\s+protection|mitigation\s+measures|residual\s+risk|Privacy\s+Act)\b

Corroborative evidence keywords

data protection impact assessments, data, protection, impact, assessments, privacy, compliance, risk

Proximity: 300 characters

Should match

• data protection impact assessments — Primary topic phrase match
• impact assessment — Case-insensitive topic phrase match
• privacy impact assessment — Alternative topic phrase match
• risk assessment — 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
• data inventory compliance — Generic word pair from old broad template should not match

Known false positives

• Common words and phrases related to data protection impact assessments 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.oaic.gov.au/privacy/privacy-guidance-for-organisations-and-government-agencies/privacy-impact-assessments
• https://www.oaic.gov.au/privacy/australian-privacy-principles-guidelines