Anonymising Pre-Sentence Reports for Training – UK GDPR-compliant anonymisation per DPA 2018

Pre-sentence reports prepared by Probation Service officers for the court contain rich personal data: the offender's offending history, family background, employment, mental health, and risk assessment scores — all constituting sensitive personal data under UK GDPR Art. 9 and DPA 2018. anonym.legal pseudonymises the individual's identifiers across the report, enabling supervisory probation officers and training providers to review assessment quality without retaining unnecessary personal data.

When this applies

This task applies when pre-sentence reports are used in probation training programmes, peer-review quality assurance, or academic research on offender assessment methodologies, and the users require the assessment content but not the offender's personal identifiers.

  1. Upload the pre-sentence report (PDF or DOCX).
  2. The engine identifies the offender's name, date of birth, address, and any family-member names referenced in the report body.
  3. Each named individual — including family members, victims, and named professionals — is pseudonymised consistently.
  4. Offending history summaries, risk assessment scores, sentencing proposals, and intervention recommendations are preserved in clear text.
  5. A reversible mapping table is produced with UK data residency.
  6. The pseudonymised report is released for training or peer-review use; the original is retained in the Probation Service case management system.

What you provide

  • Pre-sentence report (PDF or DOCX)
  • Any supplementary psychiatric or psychological report referred to in the pre-sentence report

Limitations & cautions

  • Pre-sentence reports contain special-category data under UK GDPR Art. 9 (health and mental health) and criminal-conviction data under Art. 10 — ensure the lawful basis for processing is established before any training or research use.
  • Risk assessment scores are sensitive operational data; the pseudonymised report should be shared only with those who have a legitimate training or quality-assurance purpose.

FAQ

Are named family members in a pre-sentence report pseudonymised?

Yes. Family members and other named third parties appearing in the report are detected as natural persons and pseudonymised, with distinct pseudonyms distinguishing them from the offender.

Can a pseudonymised pre-sentence report be used in an academic research project?

Yes, subject to appropriate research ethics approvals and data-processing agreements. The pseudonymised report satisfies the data-minimisation principle for research purposes, but institutional ethics obligations must be independently met.

Does the tool handle OASys risk-assessment data embedded in pre-sentence reports?

OASys score sections embedded in pre-sentence reports are preserved in full — scores, domain ratings, and assessment conclusions — with only the offender's and third parties' personal identifiers pseudonymised.

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.