What is sensitive in a pleading or affidavit?
- Party and witness identities — names, ID numbers, addresses and contact details, including third parties drawn into the case.
- Personal facts — medical, financial or family circumstances laid out in affidavits and expert reports.
- Privileged material — attorney-client communications, litigation strategy, documents filed ex parte.
- Protected proceedings — family matters, minors and sealed cases, where disclosure itself is prohibited.
What's the risk of feeding a court document into an AI tool?
For a lawyer this is a question of professional duties: attorney-client privilege and confidentiality cover everything received from the client, and sending it to an external vendor without the right arrangements is disclosure — even if no human ever reads the file. Courts and bar associations have also disciplined lawyers for filing AI-invented citations, so both the input and the output need discipline. The full picture is in AI for lawyers and confidentiality.
On top of that, party and witness details are personal data under GDPR and similar laws — and in sealed or restricted proceedings, feeding the material to an external tool may itself violate the restriction. Vendor policies differ between products and change over time; check the current policy rather than assuming.
How do you work with AI on court documents — step by step?
- Scan the document — party, witness and counsel names, ID numbers, addresses, case numbers and identifying facts.
- Replace consistently — the plaintiff is PARTY_A throughout, each witness a stable PERSON token; consistency preserves the web of testimony.
- Clean the metadata — tracked changes and comments in drafts expose strategy and client details.
- Work on the clean version — summarize a transcript, find contradictions between affidavits, map arguments, tighten drafting.
- Map back only inside the firm — the replacement map stays local, and insights reconnect to real names only on your machines.
Is an anonymized court document still useful?
Yes — legal analysis rests on the facts, the arguments and the structure, not the names. A contradiction between PERSON_002's affidavit and their testimony looks exactly the same in tokens; a weak line of argument stays weak. Consistent replacement — same witness, same token, across every document in the case — is what keeps the picture whole, as explained in the document anonymization guide.
What about published judgments?
A published judgment is a public document, and working on it with AI is a far smaller problem. But mind the difference: the pleadings, affidavits and correspondence behind the judgment are not public — and those are the files that hold your client's sensitive information.