Pseudonymising Share Purchase Agreements for External Advisers – UK GDPR-compliant anonymisation per UK GDPR Art. 5(1)(c)

A share purchase agreement (SPA) identifies the selling shareholders, their addresses, and their shareholdings in the recitals and schedule of sellers, and names warranty-givers, directors, and key employees throughout its disclosure letter and completion mechanics. anonym.legal pseudonymises these individuals so commercial counsel can advise on warranty scope, locked-box mechanics, and consideration structure without processing unnecessary personal data.

When this applies

This task applies when an SPA and its disclosure letter are shared with tax advisers, financial modellers, or secondary legal teams who need to review the commercial and financial terms but have no legitimate need to know the identities of the selling shareholders or their personal details.

  1. Upload the SPA, disclosure letter, and schedule of sellers to anonym.legal.
  2. The engine identifies selling shareholders, their addresses and shareholdings, named directors, key employees, and warranty-givers across all documents.
  3. Each natural person is pseudonymised consistently; shareholding percentages and consideration figures are preserved.
  4. Warranty scope, title protection mechanisms, locked-box or completion-accounts mechanics, and MAC definitions remain in clear text.
  5. A mapping table is produced with UK/EU data residency.
  6. Release the pseudonymised set for specialist review; restore before execution or disclosure.

What you provide

  • Share Purchase Agreement
  • Disclosure letter
  • Schedule of sellers (with share counts and consideration allocation)
  • Any employment or good-leaver / bad-leaver schedule naming key personnel

Limitations & cautions

  • The tool does not assess the commercial adequacy of warranty baskets, caps, or limitation periods — obtain specialist M&A legal advice.
  • Tax identification numbers embedded in the schedule of sellers are detected and pseudonymised; ensure the mapping table is preserved for post-completion tax filings.
  • Good-leaver / bad-leaver provisions referencing named individuals are pseudonymised; verify consistency against employment agreements after processing.

FAQ

Will pseudonymising the schedule of sellers affect consideration allocation calculations?

No. Consideration figures, shareholding percentages, and calculation mechanics are preserved in clear text. Only natural-person identifiers (names and addresses) are pseudonymised.

Can I pseudonymise a Deed of Tax Covenant attached to the SPA?

Yes. Upload it in the same batch. Named individuals in the tax covenant receive the same pseudonyms as in the main SPA.

How do good-leaver and bad-leaver clauses interact with pseudonymisation?

The commercial substance of good-leaver / bad-leaver provisions is preserved. Named individuals referenced in those provisions are pseudonymised consistently with their appearances elsewhere in the SPA.

Is this task suitable for a management buyout (MBO) structure?

Yes. MBO structures involving individual manager-shareholders are common use cases — the engine handles multiple named individuals on both buyer and seller sides.

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 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.