Anonymise Dismissal Letters for HR Training and Legal Review – UK GDPR-compliant anonymisation per ERA 1996 s.94

Dismissal letters identify the dismissed employee by name, role, and often the specific conduct or capability reason that triggered termination under the Employment Rights Act 1996. anonym.legal pseudonymises this personal data so that dismissal correspondence can be used as training examples, reviewed by external advisers, or disclosed in internal audits without revealing the individual's identity.

When this applies

Use this workflow when dismissal letters need to be shared with employment solicitors for advice, used as training precedents for HR teams, or disclosed during internal investigations where the employee's identity is not directly relevant to the recipient.

  1. Upload the dismissal letter or a set of dismissal letters to anonym.legal.
  2. The engine identifies personal data including the employee's name, job title, department, and any third-party names mentioned in the stated reason for dismissal.
  3. Each individual mentioned is pseudonymised consistently throughout the letter, including in any attached investigation summaries or hearing notes.
  4. Procedural content — dates, notice periods, appeal rights under the ACAS Code of Practice, and reference to ERA 1996 s.94 — is retained in plain text.
  5. The reversible mapping is stored encrypted with EU data residency.
  6. The pseudonymised letter is exported for sharing with solicitors or use as a training example.

What you provide

  • Dismissal letters and any associated hearing notes or investigation reports
  • Confirmation of which personal data fields should be pseudonymised
  • Any attached appeal correspondence or outcome letters

Limitations & cautions

  • anonym.legal does not assess whether the dismissal is fair or procedurally compliant under ERA 1996 s.94; legal advice remains necessary.
  • Witness names or third-party references embedded in quoted speech or investigation summaries may require manual review in addition to automated pseudonymisation.
  • The tool does not generate dismissal letters; it processes existing correspondence only.

FAQ

Will the stated reason for dismissal be retained after pseudonymisation?

Yes. The substantive reason — conduct, capability, redundancy, or other statutory reason — remains in plain text. Only the personal identifiers (name, job title, department) are pseudonymised, so the letter remains useful as a precedent or training example.

Can I use pseudonymised dismissal letters in employment tribunal proceedings?

Pseudonymised letters are useful for internal review and adviser consultation, but for tribunal bundles you must disclose the actual identity of the parties. The stored mapping key allows you to re-identify the document before including it in a formal ET bundle.

Does this cover letters for both misconduct and capability dismissals?

Yes. The workflow handles dismissal letters across all statutory reasons under ERA 1996, including misconduct, capability, redundancy, illegality, and some other substantial reason. The engine identifies personal data regardless of the grounds stated.

How does the tool handle letters that name witnesses or colleagues?

Third-party names mentioned in the letter body — witnesses, colleagues, line managers — are detected and pseudonymised alongside the dismissed employee's details. Each individual receives a distinct consistent pseudonym, preserving the narrative structure of the letter.

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.