Anonymise Reference Letters for HR Auditing and Template Use – UK GDPR-compliant anonymisation per UK GDPR Art. 6

Employment reference letters identify both the former employee and the referee by name, describe performance and conduct, and may include salary details or absence records. anonym.legal pseudonymises this personal data so that references can be audited for consistency, used as drafting templates, or benchmarked without disclosing individual employee details.

When this applies

Use this workflow when reference letters need to be reviewed for consistency across the organisation, used as drafting precedents, or shared with HR consultants where the identity of the individual subject of the reference should not be disclosed.

  1. Upload the reference letter or a batch of reference letters.
  2. The engine identifies the former employee's name, job title, dates of employment, and the referee's name and contact details.
  3. Both the former employee and the referee are pseudonymised consistently.
  4. Substantive content — performance description, conduct assessment, and factual employment history structure — is retained in plain text.
  5. The reversible mapping is encrypted and stored with EU data residency.
  6. The pseudonymised reference is exported for template or audit use.

What you provide

  • Reference letters in PDF or DOCX format
  • Indication of whether salary details or absence records should be pseudonymised

Limitations & cautions

  • anonym.legal does not assess the accuracy or legal risk of the content of a reference letter; advice from an employment solicitor is recommended before issuing references.
  • References containing evaluative opinions linked to a named individual remain personal data under UK GDPR even after pseudonymisation of the name, because the content itself is attributable to the individual in context.
  • Re-identification requires the secure retention of the mapping key.

FAQ

Will the performance assessment content be retained after pseudonymisation?

Yes. Evaluative content — descriptions of performance, conduct, and suitability — is retained in plain text. Only the names and identifiers of the employee and referee are pseudonymised, so the reference remains useful as a drafting template.

Can references be audited for consistency across different managers using this tool?

Yes. Batch pseudonymisation of references from multiple managers allows the HR function to review the language and assessments used across the organisation without revealing which specific employee each reference relates to.

Does pseudonymising a reference affect the former employee's data subject rights?

No. The original reference remains in the organisation's records. The pseudonymised copy is a data-minimised version for internal use. If the former employee makes a subject access request, the response must be based on the original records.

Are references that include salary details subject to UK GDPR?

Yes. Salary information linked to a named individual is personal data under UK GDPR Art. 6. The engine detects salary figures and can pseudonymise or mask them alongside the employee's name and other identifiers.

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.