How UK law firms adopt AI safely under SRA duties

OpenKit helps UK law firms adopt AI without breaching their SRA confidentiality and privilege duties. The short version: keep client data inside the firm’s own environment, verify every AI output against the source before it reaches a client, and record where AI was used. The tool is rarely the risk. The controls around it are what make adoption safe.

We give no legal advice and hold no legal accreditation. This is a practical guide to the technology and the governance, written for the partner or COLP who has to sign off on AI without exposing the firm.

Is it safe for a UK law firm to use AI?

Yes, when the firm treats AI output the way it treats a trainee’s first draft: useful, fast, and not to be sent to a client unchecked. The SRA regulates outcomes rather than tools, so the firm stays accountable for confidentiality and quality whether the work was done by a person or a model. Safety is a property of the workflow, not the software.

That is the whole argument of this post. The rest sets out what the SRA and Law Society actually expect, the three risks that sink firms who skip the controls, and the specific governance that makes adoption defensible at renewal.

What do the SRA and Law Society expect?

The SRA is technology-neutral: it does not ban AI, and its risk outlook on the use of AI in the legal market flags accuracy and confidentiality as the principal concerns rather than the technology itself. The firm owns the supervision question. The Law Society’s guidance, Generative AI: the essentials, turns that into a working rule.

That rule is three parts: verify AI outputs independently, disclose AI use where it is material to the advice or the client relationship, and retain records of AI interactions. Build a workflow that does those three things by default and most of the regulatory question answers itself, because the firm can show exactly what the AI did and what a qualified person checked.

The three risks that actually matter

Most of the danger in legal AI concentrates in three failure modes. Each is well documented, and each has a known control. The table below is the honest version of what goes wrong and what stops it.

The hallucination risk is not hypothetical. Courts in several jurisdictions have sanctioned lawyers for filing submissions containing case citations a general-purpose AI invented, which is why the working rule across the profession is that AI drafts and a person verifies. A system that cites its source on every output makes that verification a thirty-second check rather than a research task.

How do firms stop AI inventing fake case law?

By refusing to trust any AI output that cannot point to where it came from. The practical control is citation-anchored retrieval: the model answers only from documents the firm has given it, and every answer links back to the exact paragraph it came from, so the fee-earner verifies against the source rather than against the model’s confidence.

This is the core of how we build. On the BAiSICS commercial-lease platform, every extracted field traces to a citation in the source document, and the model is held to refusing out-of-scope questions rather than guessing. That single design choice is what separates a tool a partner will sign off from one that quietly creates liability.

How do firms keep client data confidential and in the UK?

By keeping privileged material inside the firm’s own environment and never routing it through a tool that might train on it. The control is partly contractual and partly architectural: run against models deployed in a UK or EU region under terms that forbid training on your inputs, or run open-weight models on private infrastructure the firm controls.

Where a matter or a client’s procurement contract demands it, the data never leaves the firm tenant at all. The BAiSICS platform runs entirely inside an AWS UK region, which is the kind of concrete answer a client’s data-protection officer or your professional indemnity insurer will ask for before they are comfortable.

Where AI earns its place in a law firm

Not every task needs AI, and the safest adoption starts with the work where the controls are easy and the payback is obvious. Document-heavy review, where the answer always lives inside a source the firm already holds, is the natural first use case, because citation back to source is built in.

The clearest example we can point to is BAiSICS, a custom OCR plus bespoke LLM pipeline built for commercial-lease review. It took lease review from up to four hours to roughly ten minutes at 96% accuracy against benchmarks marked up by senior partners, beating GPT-4 and the leading legal-AI products in the same test. The gain came not from a flashier model but from constraining the system to the firm’s documents and citing every field.

What governance does a firm need before it adopts AI?

Less than most firms fear, but it has to exist before the first matter touches a model. A short acceptable-use policy, a sanctioned tool so fee-earners stop reaching for consumer apps, a record of where AI is used per matter, and a named owner (usually the COLP) who can answer the SRA and the insurer. That is the floor.

OpenKit’s role is to build the tools and the audit trail that make this practical, not to act as your regulator. We hold ISO 27001, ISO 9001, and Cyber Essentials independently and operate to UK GDPR, so the security posture under the AI is documented from the start. We work alongside the firm’s COLP on supervision and design delivery to help the firm meet its own SRA obligations, never to discharge them on the firm’s behalf. For the full picture of how we approach legal work, see our legal AI page, and for where any engagement begins, our AI audit maps the workflows worth automating before anything is built.

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