Anonymising HMPPS Prison File Extracts – UK GDPR-compliant anonymisation per DPA 2018

HMPPS prison files aggregate a prisoner's reception records, adjudication history, healthcare notes, and release-planning documentation — collectively constituting a dense store of sensitive personal data under UK GDPR Art. 9 and criminal-conviction data under Art. 10. anonym.legal pseudonymises the prisoner's and third parties' personal identifiers across extracted file sections, enabling legal advisers and researchers to engage with custodial records without unnecessary personal-data retention.

When this applies

This task applies when prison file extracts are reviewed by solicitors preparing judicial review applications, by prison-law researchers, or by quality-assurance auditors examining adjudication and disciplinary records, and those reviewers require the substantive record content but not the named individual's personal identifiers.

  1. Upload the prison file extract (PDF or scan) — this may include reception records, adjudication notices, segregation orders, or release-planning documents.
  2. The engine identifies the prisoner's name, prison number, date of birth, and any named third parties — co-prisoners, witnesses to adjudications, or named staff members.
  3. All personal identifiers are pseudonymised consistently; offence descriptions, adjudication outcomes, segregation grounds, and release-plan details are preserved.
  4. Prison-establishment identifiers are preserved to maintain operational context.
  5. A reversible mapping table is produced with UK data residency.
  6. The pseudonymised extract is released for legal or research review.

What you provide

  • Prison file extract (relevant sections — reception, adjudication, healthcare, release planning)
  • HMPPS subject access response or FOI disclosure (if the extract originates from a DSAR)

Limitations & cautions

  • Healthcare sections within prison files contain special-category data under UK GDPR Art. 9 — ensure an appropriate lawful basis for any training or research processing is established independently of this tool.
  • Staff names appearing in adjudication records may be operationally sensitive — consult with the relevant prison establishment's data-protection officer before sharing pseudonymised extracts externally.

FAQ

Is a prisoner's file personal data under DPA 2018?

Yes. Prison file data constitutes both personal data and, where it relates to criminal convictions or offences, criminal-conviction data under UK GDPR Art. 10. DPA 2018 Part 3 applies additional restrictions to sensitive processing in the law-enforcement context.

Can a pseudonymised prison file extract support a judicial review application?

Judicial review applications must be filed with the real identities of the parties. The pseudonymised extract is suitable for preliminary legal advice and case preparation; re-identify before filing or lodging with the court.

Are co-prisoner names in adjudication records pseudonymised?

Yes. Named co-prisoners or witnesses to adjudication hearings are detected and pseudonymised with distinct pseudonyms, preserving the adjudication narrative without disclosing real identities.

Criminal Records

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.