Human intelligence source identities

Identifies documents containing references to human intelligence source identities 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

government-id, pii

Scope

wide

Platform compatibility

Purview: Compatible, GCP DLP: Compatible, Macie: Compatible, Zscaler: Compatible, Palo Alto: Degraded, Netskope: Unsupported

Pattern

(?is)\b(?:human\s+intelligence|source\s+identity|covert\s+source|confidential\s+informant|agent\s+handler|case\s+officer|source\s+protection|intelligence\s+asset|debriefing\s+report|source\s+recruitment)\b

Corroborative evidence keywords

human intelligence source identities, human, intelligence, source, identities, defense, government, agency, department, ministry, public sector, civil service, welfare, social services, public administration, statutory authority, regulatory body, public servant, government program, public benefit (+30 more)

Proximity: 300 characters

Should match

• human intelligence — Primary topic phrase match
• source identity — Case-insensitive topic phrase match
• covert source — Alternative topic phrase match
• confidential informant — 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
• classified contingency — Generic word pair from old broad template should not match

Known false positives

• Common words and phrases related to human intelligence source identities 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/C2004A00280/latest/text
• https://www.legislation.gov.au/C2004A04577/latest/text
• https://www.protectivesecurity.gov.au/