Pseudonymising CAFCASS Reports and Safeguarding Letters – UK GDPR-compliant anonymisation per Children Act 1989

CAFCASS reports and safeguarding letters under the Children Act 1989 identify the child, both parents, extended family, and known professionals, and disclose risk indicators from police and social services records. anonym.legal pseudonymises all named individuals while preserving the risk narrative, welfare conclusions, and recommendations so reviewing professionals can assess the analysis without direct access to personal data.

When this applies

This task applies when a CAFCASS report or section 7 safeguarding letter is shared with a jointly-instructed psychologist, independent social worker, or court expert-assessment team, and those professionals require the risk and welfare narrative but not the parties' real identities at the instruction stage.

  1. Upload the CAFCASS report or safeguarding letter to anonym.legal.
  2. The engine identifies all named individuals: child, parents, extended family, the CAFCASS officer, police contacts, and social services professionals.
  3. Each individual receives a unique, consistent pseudonym; risk indicators, welfare assessments, and CAFCASS recommendations are preserved in clear text.
  4. Police markers and social-services involvement history are preserved with individuals pseudonymised.
  5. A reversible mapping table is produced with UK data residency.
  6. Release the pseudonymised report for expert use; restore real identities before court filing or service.

What you provide

  • CAFCASS report or safeguarding letter
  • Any appended police disclosure or social services chronology
  • Expert instruction letter naming the CAFCASS officer (if relevant)

Limitations & cautions

  • CAFCASS reports contain special-category data (health, ethnic origin, criminal records) — the mapping table must be stored securely with appropriate access controls under DPA 2018 Sch.1 Pt.1.
  • The CAFCASS officer's identity is pseudonymised by default; selective re-identification via the mapping table is available if the officer's identity is required for process reasons.
  • anonym.legal does not assess the welfare conclusions or the evidential basis for risk indicators.

FAQ

Are police intelligence markers pseudonymised within the CAFCASS report?

Yes. Named individuals referenced in police markers are pseudonymised consistently with their appearances elsewhere in the report. The nature of the marker (e.g. domestic abuse flag) is preserved.

How are social services referral histories handled in the report?

Social services referral dates, referring agency names, and subject matter are preserved. Only the names of the individuals referred or referred about are pseudonymised.

Can the pseudonymised CAFCASS report be used to brief an independent social worker?

Yes. This is a primary use case. The independent social worker can form an initial view of case dynamics and welfare concerns before receiving the re-identified full bundle.

Does the tool handle CAFCASS s.16A risk assessments as well as standard reports?

Yes. Section 16A risk assessments follow the same document structure and are processed identically to standard CAFCASS reports.

Family Law

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.