Anonymize background check records for EEOC adverse-impact review – CCPA/HIPAA-compliant de-identification per 29 CFR §1607

Pre-employment background screening records subject to EEOC Uniform Guidelines at 29 CFR §1607 link applicant identity to criminal history, credit history, and other selection criteria that may produce disparate impact on protected classes. anonym.legal pseudonymizes applicant identifiers in background check records so HR and legal teams can analyze adverse-impact patterns without unnecessary exposure of individual screening outcomes.

When this applies

Apply this workflow before sharing background screening datasets with outside employment counsel for adverse-impact analysis, HR analytics teams auditing individualized-assessment compliance, or diversity consultants benchmarking selection-rate disparities across demographic groups.

  1. Upload background check reports, adjudication decision records, or applicant-tracking system exports to anonym.legal.
  2. The engine identifies applicant names, dates of birth, SSNs, and any other direct identifiers in the screening records.
  3. Each applicant is assigned a consistent pseudonymous identifier across their background check report and adjudication record.
  4. Screening-result categories (criminal record flagged, credit flag, reference outcome), adjudication decisions, and job-classification fields are retained for adverse-impact analysis.
  5. Protected-class fields, if present, are retained for demographic analysis pursuant to EEOC Uniform Guidelines §1607 adverse-impact calculations.
  6. The pseudonymized dataset is exported for adverse-impact statistical analysis or attorney review.
  7. A reversible mapping key is stored for re-identification of specific applicants if individualized-assessment re-review is required.

What you provide

  • Background check reports and adjudication decision records in PDF or CSV format
  • Applicant-tracking system exports with job-classification and disposition codes
  • Protected-class field definitions for adverse-impact analysis

Limitations & cautions

  • anonym.legal does not perform adverse-impact calculations or assess compliance with EEOC Uniform Guidelines §1607; statistical analysis must be conducted by a qualified industrial-organizational psychologist or labor economist.
  • The Fair Credit Reporting Act (15 USC §1681) governs the procurement and use of consumer background reports; FCRA obligations are distinct from and not addressed by this EEOC-focused workflow.
  • State ban-the-box and individualized-assessment laws may impose additional constraints on background screening that are not covered by this federal-level workflow.
  • Background check reports from consumer reporting agencies may contain formatting that requires supplemental manual review after automated pseudonymization.

FAQ

Can this workflow help prepare background-screening data for an EEOC conciliation or compliance review?

Pseudonymization is an internal preparation tool. For internal adverse-impact self-audits and attorney-review stages, pseudonymized datasets are appropriate. Actual submissions to the EEOC or agency conciliation proceedings may require identified records; confirm with counsel before submitting.

Will criminal-record categories be retained after pseudonymization?

Yes. Screening-result categories and adjudication codes are treated as structural data and are retained for adverse-impact analysis. Only the applicant's personal identifiers are pseudonymized.

Can the tool process background check records across multiple hiring locations for systemic analysis?

Yes. Batch processing allows HR teams to pseudonymize background check records from multiple locations or hiring managers simultaneously, enabling systemic adverse-impact analysis across the full applicant population without exposing individual applicant identities.

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.