Pseudonymising OASys Offender Management Assessments – UK GDPR-compliant anonymisation per DPA 2018

OASys (Offender Assessment System) reports assess an offender's criminogenic needs, risk of serious harm, and sentence-management requirements across twelve domains. These assessments carry substantial personal data — including health, substance misuse, and family circumstances — classified as both sensitive personal data under UK GDPR Art. 9 and criminal-conviction data under Art. 10. anonym.legal pseudonymises the offender's identifiers to enable quality-assurance review without personal-data retention.

When this applies

This task applies when OASys assessments are reviewed by probation quality-assurance teams, training supervisors, or research bodies analysing risk-assessment methodology, and those reviewers require the assessment data but not the offender's personal identifiers.

  1. Upload the OASys assessment report (PDF or structured export).
  2. The engine identifies the offender's name, date of birth, CRN (case reference number), and any named third parties — victims, family members, or co-defendants — in the narrative sections.
  3. All personal identifiers are pseudonymised consistently across the twelve domain sections.
  4. Domain scores, risk-of-serious-harm ratings, sentence-plan objectives, and intervention recommendations are preserved in clear text.
  5. A reversible mapping table is produced with UK data residency.
  6. The pseudonymised assessment is released for quality-assurance or training review.

What you provide

  • OASys assessment report (all domains)
  • Sentence plan or supervision record linked to the OASys assessment

Limitations & cautions

  • OASys data is operationally sensitive — even pseudonymised assessments should be shared only within the authorised training or quality-assurance cohort.
  • The tool pseudonymises personal identifiers but does not validate the accuracy of the risk-assessment scores or domain ratings — assessment quality review requires specialist probation expertise.

FAQ

Are victim references in the OASys assessment pseudonymised?

Yes. Named victims appearing in OASys narrative sections are detected and pseudonymised with distinct pseudonyms, preserving the victim-offender relationship context without disclosing real identities.

Can pseudonymised OASys data be used in academic criminology research?

Yes, subject to appropriate ethical approvals, data-sharing agreements with HMPPS or the Probation Service, and compliance with DPA 2018 requirements for research processing. The pseudonymised data satisfies data-minimisation obligations for the research context.

Does the tool handle OASys updates and re-assessments in a longitudinal series?

Yes. Upload the full longitudinal series in a batch; the engine assigns the same pseudonym to the offender across all assessment versions, preserving longitudinal coherence.

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.