Anonymize overtime classification audits for FLSA exempt-status review – CCPA/HIPAA-compliant de-identification per FLSA §207

FLSA §207 requires overtime pay for non-exempt employees and sets criteria for white-collar exemptions that hinge on salary level and job duties. Audit datasets correlating employee duties descriptions with pay classifications carry personal data that must be protected when shared for legal review. anonym.legal pseudonymizes employee identifiers so exempt-status audits can proceed without unnecessary privacy exposure.

When this applies

Apply this workflow when duties questionnaires, job-description analyses, and salary-basis records must be shared with outside counsel or DOL audit respondents for FLSA exemption-status review or defense.

  1. Upload duties questionnaires, job descriptions, pay-rate records, and salary-basis certifications to anonym.legal.
  2. The engine identifies employee names, employee IDs, manager names, and compensation figures linked to individuals.
  3. Each employee is pseudonymized consistently across all related documents in the audit package.
  4. Duties descriptions, salary levels, job titles, and supervisory authority narratives are retained as structural content for exemption analysis.
  5. The pseudonymized audit package is assembled and exported for attorney or DOL response use.
  6. A reversible mapping key is stored for re-identification of specific employees if individualized exemption determinations are required.

What you provide

  • Duties questionnaires and completed job-description forms in PDF or DOCX format
  • Pay-rate and salary-basis records in CSV or XLSX format
  • Scope definition: single job classification or multi-classification audit

Limitations & cautions

  • anonym.legal does not determine whether any employee qualifies for an FLSA exemption; that legal determination requires attorney or DOL guidance.
  • The salary-level threshold under FLSA §207 changes by regulation; the current applicable threshold must be verified independently before any legal conclusion is drawn.
  • State overtime laws may provide greater protections than FLSA §207; this workflow covers federal requirements only.
  • Duties descriptions written in first-person by employees may inadvertently include identifying anecdotes requiring manual review.

FAQ

Can the tool process duties questionnaires for an entire job classification at once?

Yes. Batch processing allows you to pseudonymize all questionnaires for a given job title or pay grade simultaneously, enabling counsel to analyze exemption-status consistency across the classification.

Will the salary figures in pay-rate records be preserved after pseudonymization?

Yes. Salary amounts and pay-rate data are treated as structural content and are retained. Only the employee identifiers linking those figures to a named individual are pseudonymized.

Is this workflow suitable for responding to a Department of Labor wage-and-hour investigation?

Yes. Pseudonymizing the audit dataset before sharing with outside counsel allows your legal team to assess your FLSA exposure without the review team unnecessarily accessing personal data for employees not under investigation.

Employment Law

About this page

We update this page when our platform or the law changes.

Read our founder note for how we work.

Each change shows up in the timestamp at the top.

We follow these rules

  • GDPR (EU 2016/679).
  • ISO/IEC 27001:2022.
  • NIS2 (EU 2022/2555).
  • HIPAA safe harbor under 45 CFR § 164.514(b)(2).

Our promise

We do not sell your data.

We do not train models on your text.

We store your files in Germany.

You can delete your account at any time.

You own your work.

Where we run

Our servers live in Falkenstein, Germany.

We use Hetzner. They hold ISO 27001 certification.

All data stays in the EU.

Backups run every day.

Need help?

Email support@anonym.legal.

We reply within one business day.

How we test

We run a full check suite on every release.

Each surface gets its own sweep script and report.

Human reviewers spot-check the output each week.

We track recall and precision on a labelled set.

Bad runs block the deploy.

What we never do

  • We never sell your information to third parties.
  • We never train models on what you upload.
  • We never keep your work after you delete it.
  • We never share keys with any outside firm.
  • We never run ads inside the product.

Plans in plain words

We sell credits, not seats.

One credit covers one short job.

Long jobs use a few credits each.

You can top up at any time.

Unused credits roll over each month.

Read the plans page for current rates.

Who built this

A small team of engineers and lawyers built this.

We ship from Europe and work in the open.

Our founder note spells out why we started.

Where to start

How the parts fit

A browser add-on cleans text inside Chrome.

A Word plug-in handles drafts in Office.

A small desktop tool works on whole folders.

An agent protocol link feeds large models safely.

All four share one core engine and one rule set.

Words from our team

We started this work after a lunch about cookies.

One friend kept getting odd ads on her phone.

We asked why a court file leaked through a draft.

We sketched the first build on a napkin that week.

By month three we had a tiny demo for a friend.

She used it on her first case the next day.

Common questions we hear

Can the tool read scanned PDFs? Yes, with OCR.

Does it work on long files? Yes, in small chunks.

Can I roll my own rule set? Yes, save it as a preset.

Does it run offline? The desktop build runs offline.

Do you keep my files? No, the cloud build wipes after each run.

Will it learn from my work? No, we never train on inputs.

A short tour of the workflow

Upload a file or paste a snippet of prose.

Pick the entities you want gone from the draft.

Choose a method: replace, mask, hash, encrypt, or redact.

Press run and watch the side panel show each hit.

Skim the result and tweak any rule that misfired.

Save the cleaned file or send it to a teammate.