Retention and Deletion of AI Outputs on Matters

Once AI starts producing real work product — notes, drafts, summaries, time suggestions — you need to answer some unglamorous but important questions:

• How long do we keep this stuff?
• Where do we keep it?
• When do we delete it — and how do we prove that we did?

This is not just a GDPR or IT housekeeping issue. Retention and deletion of AI outputs affects:

• privilege and confidentiality;
• auditability and supervision; and
• how cluttered (or not) your matter files become.

This article sets out a practical approach to retention and deletion of AI outputs on matters for UK firms.

1. Decide what counts as an “AI output” for retention purposes

Not everything an AI tool produces is equal. You might distinguish between:

• Ephemeral prompts and scratch-pad outputs — “what-if” explorations that never influence client work.
• Internal working documents — summaries, chronologies, draft notes used in supervision or decision-making.
• Client-facing drafts — AI-generated documents that are then edited and sent to clients or courts.
• System logs — metadata about AI use (who did what, when, with which documents).

Your retention approach should treat these categories differently. In particular:

• final client documents belong in your usual matter and document retention scheme;
• AI system logs may need longer retention to support audits and investigations;
• scratch content may be deletable much earlier, provided it truly never influenced outcomes.

2. Anchor AI outputs to the matter file

To keep retention sane, think in terms of matter-centric records, not AI-centric silos. That means:

• where an AI output affects a matter (for example, a summary used in advice), it should be saved into the matter file;
• when a matter reaches the end of its life cycle, AI outputs stored with it should follow the same retention and deletion rules as other matter documents;
• you avoid having important AI-generated material stranded in separate tools that are not subject to normal file closure processes.

This approach also makes it easier to comply with subject access requests and litigation holds — you know where relevant information lives.

3. Set explicit retention for AI system logs

Logs of AI activity (for example, “email thread summarised by X on date Y”) are different. They:

• help you supervise AI use;
• may be relevant to regulatory or disciplinary questions;
• can support insurers or complaints investigations.

For these, you might choose to:

• retain them for at least as long as the underlying matters, or for a standard period aligned with your risk appetite;
• store them in a way that allows searches by matter, user and date;
• ensure that deletion is controlled and auditable.

You do not need to keep every token of model input and output forever. But you do need enough logging to show how AI was used if questions arise later.

4. Minimise unmanaged copies

A common problem is AI outputs being:

• copied into personal notes apps;
• saved on local drives;
• left in screenshots or emails.

To reduce this:

• encourage (or require) users to generate AI outputs inside your core systems (case management, DMS, Microsoft 365) rather than consumer tools;
• make it easy to save AI drafts directly into the matter file;
• discourage manual copy-paste between systems unless necessary for client work.

The more centralised your AI workflows are, the easier retention and deletion become.

5. Align AI retention with your wider policies

You probably already have policies covering:

• matter file retention and destruction;
• email archiving;
• system logging and monitoring;
• data protection and subject access.

AI retention should be woven into these, not bolted on separately. For example:

• add explicit references to AI-generated notes and drafts in your file destruction procedures;
• specify how AI logs are treated in your IT/logging policies;
• clarify what happens to AI data if you change vendors or systems.

The aim is coherence: anyone reading your policies should see AI as just another source of records, handled on purpose.

6. Plan for vendor exit and data portability

If you use third-party AI platforms, you need to know what happens when you leave. Questions include:

• “Can we export our AI outputs and logs in usable formats?”
• “What data does the vendor delete and when?”
• “How do we get evidence of deletion (for example, certificates or logs)?”

Your contracts should match your retention policies, not the other way round. If a vendor cannot accommodate your basic needs on export and deletion, factor that into your risk assessment.

7. Balance “delete when no longer needed” with real-world risk

Data protection law pushes you towards minimisation and timely deletion. Professional and practical realities pull in the other direction:

• clients may complain years later;
• insurers may ask for old files;
• regulators may investigate historic behaviour.

A sensible balance often looks like:

• keeping matter-centric AI outputs for as long as you keep the rest of the file;
• retaining AI system logs for a defined period aligned with those risks;
• genuinely deleting scratch-pad material that never fed into decisions, once it is clearly no longer needed.

The key is to decide this deliberately — and then follow your own rules consistently.

OrdoLux is legal case management software built for UK solicitors, with AI tools integrated directly into the matter workspace.

Where OrdoLux fits

OrdoLux stores all matter activity — time entries, documents, emails and AI research outputs — within the matter record. That gives firms a single place to review what happened on a file, which supports supervision and audit.

The platform includes 2FA for access control, KYC/AML via Checkboard, and client account reconciliation. The built-in AI research tool includes citations so outputs can be verified before use.

See all features or book a demo.