AI Governance for UK SMEs
Sized for a small team.

The UK employs a principles-based, decentralised approach to AI governance, emphasising proportionality and innovation. Unlike the prescriptive EU AI Act, UK SMEs are governed by existing regulators (ICO, FCA) enforcing core principles like fairness and accountability. For business leaders, this means compliance is outcome-focused, often requiring frameworks like ISO 42001 to demonstrate "Duty of Care" under the Companies Act 2006.

Six things. Get them in place and you're ahead of most companies ten times your size.

1. An AI policy your staff will actually read. One or two pages.
2. A data boundary. What's fine to share with AI, what never leaves the building.
3. An approval route. Who says yes to a new tool, and how.
4. A risk register. The handful of things that could go wrong, and who owns each.
5. A DPIA where personal data is involved. Required under UK GDPR for higher-risk processing.
6. Evidence. Dated records, so you can prove any of the above on request.

Read next: Is AI use covered by UK GDPR? · Do UK SMEs need ISO 42001? · Who provides AI data protection audits? · How to write an AI policy

• Do they work at SME scale, or is it Big-4 governance built for banks?
• Is a senior person doing the work, or a junior with a template?
• Do they know the actual standards: ISO 42001, NIST AI RMF, CDEI assurance?
• Do you get evidence you can reuse, or a report for a drawer?
• Can they train your team, so the policy survives real work?

Ask those of anyone you're considering, us included. Every price is on our pricing page.

Most UK SMEs need more than one framework. The Technical Governance package selects and implements the right combination for your jurisdictional exposure rather than locking you into a single standard. Most clients run more than one in parallel, so scope depends on how many frameworks and jurisdictions are in play.

Governance starts with culture. Before we touch the code, we install the "Shield," the "Heart," and the "Engine."

1. The Shield (Safety), governance and risk. If you don't have an AI strategy, you have a security breach. We apply military-grade COMSEC principles to protect IP and cut "Shadow AI" risk.

2. The Heart (Culture), education and buy-in. The barrier isn't code; it's fear. We act as Cultural Transformation Guides, moving your workforce from the "fear zone" to the "growth zone" via our 90-day CHANGE programme.

3. The Engine (System), automation and agents. We audit workflows to identify low-value admin. We replace manual drudgery with AI agents, freeing leadership to focus on strategy.

An AI policy is the written statement of what your organisation will and will not do with AI, which data may be used where, and who decides. Most fail for the same reason: they are written as prohibitions by someone who is not doing the work, so they describe a business the team does not recognise and get ignored within a month. We write policy from what is already happening, which means starting with a look at the AI use you have rather than the AI use you assume.

Risk management here means identifying where an AI system could produce a materially wrong outcome, deciding which of those you will accept and which you will control, and recording the reasoning. A risk register that is never revisited is documentation. One reviewed on a cadence, with named owners, is governance. Where the processing warrants it, that includes a Data Protection Impact Assessment (DPIA) rather than an assumption that one is not needed.

A DPIA is a Data Protection Impact Assessment. UK GDPR requires one before you start higher-risk processing of personal data, which a lot of AI use counts as. It's a structured way to spot and reduce the risk before it bites.

An AI agent decides its own steps toward a goal, which is precisely what makes it useful and precisely what makes it harder to govern than a chatbot. The controls that matter are different: what systems it may reach, what it may do without a human, what it must log, and how you would stop it. Most AI policies written before agents existed do not answer any of those, and were never intended to.

Tools that read your files and act on them raise a different question from tools you paste text into. Claude works against a folder on a real machine; Claude Code operates on real repositories. The governance question is which folders, whose machines, what the retention posture of the specific tier in use actually is, and what evidence you keep. Those are answerable, and they should be answered before rollout rather than during an incident.

You cannot govern a process you cannot describe. Where a workflow is about to have AI applied to it, mapping how it currently runs is frequently the step that exposes the real control gap, and it does so before any technology is introduced rather than after.

Ignite AI Solutions is a UK member of Anthropic's Claude Partner Network. Our governance work draws on Anthropic's own published material. Our ISO 42001 control mapping references Anthropic's deployment guidance, and our AI Manifesto templates include red lines drawn from Anthropic's Acceptable Use Policy. The result is governance built around how Claude is trained, deployed and audited, rather than a generic governance product retrofitted to AI.

We run that work from our UK company and our UAE FZCO, so clients with exposure in both jurisdictions get one governance approach. We take no commission on licences from any vendor.