Anonymise Redundancy Selection Criteria Records for Legal Review – UK GDPR-compliant anonymisation per ERA 1996 s.139

Redundancy selection matrices score named employees against criteria such as attendance, skills, and performance, creating personal data that is sensitive during a collective or individual redundancy process. anonym.legal pseudonymises these records so that selection criteria and scoring can be reviewed by advisers or challenged in proceedings without prematurely disclosing which employees scored lowest.

When this applies

Use this workflow during a redundancy process governed by ERA 1996 s.139 when selection matrices need to be shared with legal advisers, trade union representatives, or internal audit teams before the outcome is communicated to affected employees.

  1. Upload the redundancy selection matrix or scoring spreadsheet.
  2. The engine identifies employee names, job titles, employee numbers, and scores linked to identifiable individuals.
  3. Each employee in the matrix is pseudonymised consistently, preserving the relative ranking and scoring structure.
  4. Selection criteria labels, scoring weights, and aggregate statistics are retained as non-personal structural data.
  5. The reversible mapping is encrypted and stored with EU data residency.
  6. The pseudonymised matrix is shared with advisers or trade union representatives for procedural review.
  7. Before communicating outcomes to employees, the matrix is re-identified using the stored key.

What you provide

  • Redundancy selection matrix or scoring spreadsheet
  • List of employees at risk and their employee numbers
  • Selection criteria definitions and scoring weights

Limitations & cautions

  • anonym.legal does not assess whether the selection criteria are fair, objective, or compliant with the Equality Act 2010; that remains the employer's legal responsibility.
  • Where selection criteria themselves contain inherently identifying information — such as a unique qualification held by only one employee — manual review is required.
  • Re-identification for the purpose of communicating outcomes to employees requires the secure retention of the mapping key.

FAQ

Will relative scores and rankings be preserved in the pseudonymised matrix?

Yes. The numerical scores, ranking positions, and comparative data are retained in full. Only the personal identifiers linking scores to named individuals are pseudonymised, so the matrix remains useful for procedural review.

Can the matrix be shared with a trade union representative in pseudonymised form?

Yes. Sharing the pseudonymised matrix with trade union representatives during consultation is a common use case. It enables representatives to scrutinise the selection methodology without prematurely disclosing which individual employees have been selected.

Does pseudonymising the matrix affect the employer's collective consultation obligations?

No. The pseudonymisation is a data-minimisation measure for sharing purposes. The employer's obligations under ERA 1996 to consult individually and collectively about redundancy selection are separate and unaffected by how the matrix is shared internally.

What if the selection criteria include absence records that may relate to disability?

Attendance or absence data that could disclose a disability is treated as special category data under UK GDPR Art. 9 and the Equality Act 2010. The engine flags such data for enhanced pseudonymisation, and you should review the output to ensure compliance before sharing.

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.