Anonymising Mutual NDAs Before External Review – UK GDPR-compliant anonymisation per UK GDPR Art. 4(1)

A mutual non-disclosure agreement is a bilateral confidentiality contract under which 2 parties exchange protected information — the Trade Secrets (Enforcement, etc.) Regulations 2018 implement the EU Trade Secrets Directive into UK law and provide statutory remedies, while UK GDPR violations can attract fines up to £17.5 million or 4% of annual global turnover. anonym.legal pseudonymises named signatories so commercial terms can be reviewed without exposing individual identities.

When this applies

This task applies when both parties need to circulate the NDA to advisers or auditors who require sight of commercial terms but have no lawful basis to process named individuals' personal data. According to the ICO, the data-minimisation principle under UK GDPR Art. 5(1)(c) — in force since 2018 — requires that only necessary personal data be shared; in 2020 alone the ICO issued fines totalling over £38 million across major enforcement cases including £20 million against British Airways and £18.4 million against Marriott International.

  1. Upload the mutual NDA (PDF, DOCX, or ODT) to anonym.legal; the document structure, clause numbering, and formatting are preserved.
  2. The engine detects personal-data entities across all 267+ supported categories — full names, job titles, email addresses, and postal addresses of signatories and contacts.
  3. Each named individual is assigned a consistent pseudonym (e.g. 'Party A Representative 1') applied uniformly across recitals, signature blocks, and schedules.
  4. Commercial terms — confidentiality scope, duration, permitted-purpose carve-outs, and governing-law clauses — remain in clear text.
  5. A reversible mapping table is generated and stored with EU/UK data residency, allowing full re-identification before filing or execution.
  6. Download the pseudonymised draft for circulation, and use the mapping key to restore original names when the final signed copy is required.

What you provide

  • Signed or draft mutual NDA document
  • List of all named signatories and their roles (to verify entity detection)
  • Any schedule or exhibit appended to the main agreement

Limitations & cautions

  • anonym.legal pseudonymises personal data but does not review the legal sufficiency of the confidentiality obligations — obtain independent legal advice on clause scope. Any liability-exclusion clause in the NDA itself is subject to the reasonableness test under Unfair Contract Terms Act 1977 s.3 where one party deals on the other's written standard terms.
  • Handwritten or heavily scanned documents may require OCR pre-processing before entity detection achieves full coverage.
  • Re-identification requires secure custody of the mapping key; loss of the key makes reversal impossible.
  • The Limitation Act 1980 provides a 6-year limitation period for simple contract claims; ensure the mapping key is retained for at least that period after execution.

FAQ

Will pseudonymisation affect the enforceability of the NDA?

The pseudonymised version is a working copy for review purposes only. Before execution or filing, re-identify the document using the mapping key so that the executed version bears the correct legal names. Only the identified version has contractual effect.

Does the engine handle NDAs with multiple signatories on each side?

Yes. Each natural person is tracked as a distinct entity and assigned a unique, consistent pseudonym throughout the document, regardless of how many individuals are listed on either side.

Is the mapping table stored in the UK or EU?

All processing and storage occurs within UK/EU data residency boundaries, in line with UK GDPR requirements for personal data transfers. According to the Data Protection Act 2018, Schedule 21 governs international transfers.

What statutory protection applies to the confidential information in an NDA?

The Trade Secrets (Enforcement, etc.) Regulations 2018 protect trade secrets that meet the criteria of being secret, having commercial value, and being subject to reasonable steps to maintain secrecy. The Misrepresentation Act 1967 also applies if any pre-contractual statements induced entry into the NDA.

Can I redact company names as well as personal names?

By default the engine targets natural-person data. You can manually flag company names as additional entities to pseudonymise if your review scenario requires it.

Commercial Contracts

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 company HQ is in Saarbrücken, Germany. Our servers run in Hetzner's Falkenstein datacenter.

Hetzner holds 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.