Logging AI Use on Matters: Audit Trails That Supervisors Actually Read

If your firm is going to lean on AI for real work, you need to be able to answer three simple questions:

How was AI used on this matter?
Who checked what it produced?
What did we do when it went wrong?

Regulators, courts, insurers and sophisticated clients will all care about these points. The good news is that you do not need a complex system to get them right — but you do need something better than “we don’t really know”.

This article sets out a practical model for logging AI use on matters, focused on UK firms that want audit trails supervisors will actually read.

1. Decide what you care about logging

Start by agreeing, in principle, what you want the record to show. Typical elements include:

When AI was used (date and time);
Who used it (user ID);
What for (task type, such as “email summary”, “first-draft advice”, “chronology extraction”);
What it touched (documents, emails, note IDs);
How it was checked (reviewer, sign-off notes);
What went wrong (if anything) and how it was corrected.

You do not need to log every keystroke. Aim for a level of detail where, if a regulator or insurer asks about a matter, you can reconstruct:

the main AI-assisted steps; and
the human supervision applied.

2. Keep logs close to the matter file

Logs are useless if they live in a separate system nobody checks.

Better pattern:

treat AI activity as part of the matter record, alongside documents, emails, notes and time entries;
display recent AI actions in a simple list on the matter overview screen;
let supervisors drill down into specific events when reviewing files.

For example, a matter timeline might show:

“09:12 – AI summary of email thread [link] created by AB”
“09:30 – Chronology updated from AI extraction of documents [list] – reviewed by CD”
“10:05 – First-draft advice letter generated via AI – edited and approved by EF”

This is far more useful than a separate, obscure log buried in an admin dashboard.

3. Standardise “reason codes” for AI use

Free-text notes are helpful, but for analytics and risk monitoring you also need structured data.

Create a short list of AI “reason codes”, such as:

RESEARCH_ORIENTATION
DRAFTING_FIRST_PASS
EMAIL_SUMMARY
CHRONOLOGY_UPDATE
ATTENDANCE_NOTE_STRUCTURING
TIME_CAPTURE_SUGGESTION

When a user runs an AI action, the system can either:

infer the reason code automatically (for predefined workflows); or
prompt the user to choose from a dropdown.

This makes it much easier to answer questions like:

“How often are we using AI for research vs drafting?”
“Which departments rely most on AI for client communication drafts?”

4. Capture supervision without creating busywork

Partners do not have time to write essays for every AI-assisted draft. But they do need to be visibly in charge.

A simple approach is:

when a supervisor reviews an AI-assisted output, they tick one of a few options, for example:
- “Reviewed – accepted with minor edits”
- “Reviewed – significant edits made”
- “Reviewed – rejected output”
optionally add a short free-text note for unusual cases (“Hallucinated case law – corrected and added to training materials”).

This creates a light-touch supervision record that:

shows someone senior has looked at key outputs; and
generates useful data on where AI is working well or badly.

5. Record problems as learning events, not secrets

It is unrealistic to expect AI never to go wrong. What matters is:

how quickly you catch issues; and
whether you learn from them.

Logging should therefore include a simple mechanism to flag:

hallucinations (made-up cases, misquotes);
tone problems (too aggressive, too informal);
confidentiality concerns (prompt included more data than necessary).

For example:

“Flag this output as a problem” button on AI results;
short reason list and optional description;
automatic notification to someone in risk/innovation.

Over time, this allows you to tweak prompts, policies and training, backed by real evidence.

6. Align logs with your wider governance

Logging AI use is not an isolated activity. It should connect to:

your AI policy (what is allowed and prohibited);
your AI risk assessment (where the biggest risks lie);
your training (for fee-earners, PAs and support staff);
your incident and complaints processes.

Practical examples:

if logs show heavy AI use in research tasks, you might focus training on verification and hallucinations;
if a complaint involves alleged AI misuse, you should be able to pull the relevant log entries quickly;
if regulators ask how you supervise AI, logs help you demonstrate real oversight, not just paper policies.

Where OrdoLux fits

OrdoLux is being built on the assumption that firms will need matter-level audit trails for AI:

AI actions (summaries, drafts, extractions) are recorded as part of the matter timeline;
each entry shows who triggered it, which documents it touched, and any supervision notes;
logs can be filtered by user, task type or date for internal reviews and external enquiries.

Because everything sits inside the same system as your documents, emails and time entries, supervisors and COLPs see:

not just that “AI was used”, but how, where and under whose oversight.

That makes it much easier to use AI confidently — and to explain your approach to anyone who asks.

This article is general information for practitioners — not legal advice, regulatory advice or a substitute for your own governance design.

Looking for legal case management software?

OrdoLux is legal case management software for UK solicitors, designed to make matter management, documents, time recording and AI assistance feel like one joined-up system. Learn more on the OrdoLux website.