Anonymise Performance Reviews for HR Analysis and Training – UK GDPR-compliant anonymisation per UK GDPR Art. 5

Performance reviews record named employees' ratings, development goals, and manager commentary, creating personal data that identifies the subject and often the reviewer. anonym.legal pseudonymises these records so that performance data can be analysed for organisational trends, used in management training, or benchmarked externally without disclosing individual employee identities.

When this applies

Use this workflow when performance review data needs to be shared with external analysts, used in management-skills training, or aggregated for reporting purposes where individual identification is not required.

  1. Upload the performance review documents or a batch export from your HR system.
  2. The engine identifies names, employee numbers, reviewer names, and any personally identifying commentary within the review text.
  3. Each employee and reviewer is pseudonymised consistently, preserving the relationship between reviewer and reviewee without revealing identities.
  4. Ratings, scoring frameworks, competency labels, and developmental commentary are retained as structural content.
  5. The reversible mapping is encrypted and stored with EU data residency in line with UK GDPR Art. 5 storage-limitation and integrity principles.
  6. The pseudonymised reviews are exported for analysis or training purposes.
  7. For aggregated trend analysis, multiple pseudonymised reviews can be processed together to identify patterns without individual identification.

What you provide

  • Performance review documents or HR system exports
  • Confirmation of which fields (name, employee number, reviewer name) should be pseudonymised
  • Specification of any free-text fields that may contain identifying commentary

Limitations & cautions

  • The engine may not detect highly contextual identifiers in free-text commentary — such as references to a unique project or a very small team — without manual review.
  • anonym.legal does not assess the fairness or accuracy of performance ratings; that remains an HR and management responsibility.
  • Re-identification of an employee's review for disciplinary or capability purposes requires the secure retention of the mapping key.

FAQ

Will ratings and competency scores be preserved after pseudonymisation?

Yes. Numerical ratings, competency labels, and scoring frameworks are retained in plain text. Only personal identifiers — names, employee numbers, and personalised commentary — are pseudonymised, so the review data remains analytically meaningful.

Can multiple years of review data be processed together for longitudinal analysis?

Yes. Batch processing maintains consistent pseudonyms for the same individual across multiple review cycles, so year-on-year performance trends can be analysed without re-identifying the employee.

Does the tool identify the reviewer's name as personal data?

Yes. The reviewer's name and any identifying commentary that could identify the reviewer are pseudonymised alongside the reviewee's details. This protects the privacy of managers involved in the review process.

Is performance data classed as special category data under UK GDPR?

Performance data is generally ordinary personal data rather than special category data under UK GDPR Art. 9. However, where reviews reference health conditions, disability adjustments, or trade union activities, those elements are treated as special category data and pseudonymised with appropriate care.

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.