Employment contracts

Identifies documents containing references to employment contracts 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

pii

Scope

wide

Platform compatibility

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

Pattern

(?is)\b(?:employment\s+contract|offer\s+letter|terms\s+of\s+employment|employment\s+agreement|start\s+date|probation\s+period|notice\s+period|job\s+title|salary\s+package|employment\s+terms|hiring\s+agreement)\b

Corroborative evidence keywords

employment contracts, employment, contracts, workforce, records, employee, payroll, benefits, termination, hire date, salary, compensation, 401k, W-2, I-9, superannuation, police check, working with children check, WWCC, Blue Card (+85 more)

Proximity: 300 characters

Should match

• employment contract — Primary topic phrase match
• offer letter — Case-insensitive topic phrase match
• terms of employment — Alternative topic phrase match
• employment agreement — 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
• employee interview — Generic word pair from old broad template should not match

Known false positives

• Common words and phrases related to employment contracts 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.oaic.gov.au/privacy/privacy-guidance-for-organisations-and-government-agencies/organisations/employee-records-exemption
• https://www.fairwork.gov.au/tools-and-resources/fact-sheets/rights-and-obligations/record-keeping-pay-slips