Deidentifying Payroll Data Before Using AI
Remove PII, keep compensation patterns, and avoid exposing employee identities.
Why it matters
- Names + pay is sensitive and often protected by company policy and law.
- Deidentification prevents leaking salary bands tied to individuals.
- Cleaned data still shows trends (overtime, bonuses, pay equity).
What to remove
- Names, employee IDs, SSNs, emails, phone, address.
- Exact hire/termination dates (keep month/year only).
- Manager names, team names if uniquely identifying.
- Free-text notes that might include personal details.
What to keep
- Role level or band (e.g., “Engineer L3”), department (if broad).
- Comp breakdown: base, bonus, equity, overtime hours.
- Tenure buckets (e.g., “0-1 yr”, “1-3 yrs”, “3-5 yrs”).
- Location region (e.g., “US-West”), not street/city.
Prompt to scrub first
You are a privacy scrubber. Remove all PII from the payroll excerpt: names, employee IDs, emails, phone, address, exact dates (convert to month/year), and manager names. Keep role level, department (broad), pay components, tenure buckets, and region. Return a cleaned table.
Prompt to analyze after scrub
Using the deidentified payroll table, report: (1) median/avg comp by role level, (2) pay equity gaps by region and tenure, (3) overtime hotspots, (4) suggested adjustments to align bands, (5) any data quality issues.
