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Deidentifying Payroll Data Before Using AI

How to deidentify payroll data before AI analysis. What to strip, what to keep, and prompts you can use safely.

Published: November 26, 2025

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.