A straight-talking guide for UK SMEs using (or planning to use) generative AI in day-to-day operations—without turning your business into an easy target.
Answer (in plain English): An AI security consultant helps your business use AI safely by identifying where AI touches sensitive data, testing for AI‑specific threats (like prompt injection and data leakage), and putting controls in place—policies, access rules, monitoring and incident plans. UK SMEs should consider one before rolling out generative AI to staff or customers.
Here’s why this matters right now: just over four in ten UK businesses (43%) reported a cyber security breach or attack in the last 12 months.[1] That’s before you add in the new “AI-shaped” attack surface created by chatbots, copilots, automation agents, integrations, and staff experimenting with “whatever AI tool looks helpful”.
If you’re planning a safe rollout (use cases, data boundaries, governance, and the guardrails that stop things getting weird), take a look at our AI consultancy and implementation approach. It’s built for SMEs who want progress without the risk hangover.
An AI security consultant’s job is to reduce the risk created by AI systems and AI-assisted workflows—without killing the value that made you adopt AI in the first place. In practice, that usually means focusing on three areas:
| Focus | Traditional cyber security | AI security |
|---|---|---|
| Attack surface | Networks, endpoints, apps, identity | Prompts, agents, plugins, model supply chain, data flows |
| Common failures | Misconfigurations, phishing, unpatched systems | Data leakage, prompt injection, unsafe automation, shadow AI |
| Controls | MFA, EDR, firewalls, patching, backups | AI usage policy, prompt/data controls, model access, red teaming, monitoring |
| Success metric | Reduce breaches and downtime | Use AI safely without losing data, trust, or compliance |
What they don’t do (or shouldn’t): sell you a shiny AI tool and call it “security”, gloss over your data handling, or treat AI risk like it’s just another firewall rule.
AI changes the shape of risk. The classic cyber story is: attacker breaks in, steals data, you clean up the mess. With AI, the more common SME story is: a well-meaning employee pastes sensitive info into an AI tool, or an automation agent takes an action you didn’t expect, or a chatbot is tricked into revealing what it shouldn’t.
Big picture: data breaches are still expensive (IBM puts the global average at $4.4M).[2] But the AI twist is the “oversight gap”: IBM’s 2025 report also flags that 63% of organisations lacked AI governance policies, and 97% that reported an AI-related incident lacked proper AI access controls.[2]
If your AI rollout relies on “we’ll sort it later”, you’re betting your reputation on luck. A practical approach is to build guardrails first, then scale usage.
There are loads of AI risks. Most SMEs don’t need to tackle all of them at once. Start with the ones that cause real damage quickly:
A practical starting point for AI-specific threats is the OWASP Top 10 for LLM Applications (dated 17 November 2024).[5] For UK-focused baseline controls, the government’s AI Cyber Security Code of Practice (published 31 January 2025) sets out measures to address risks to AI systems.[4]
If you want something you can actually implement (not a 94-page policy document), use this as your starting roadmap. You can do a lot in 30 days.
For a broader risk lens (security, privacy, accountability, and governance), a well-known framework is the NIST AI Risk Management Framework (AI RMF 1.0) (January 2023).[6] It’s not UK-specific, but it gives you a practical way to organise risk work without getting lost.
Good engagements are structured, time-boxed, and end with something you can actually operate. Here’s what you should expect:
If you want a sense of the broader threat landscape, Verizon’s 2025 DBIR analysed 22,052 real-world incidents and 12,195 confirmed data breaches, with victims from 139 countries.[3] Translation: it’s not theoretical.
SMEs don’t need the “big four theatre”. You need someone who can understand your workflows, spot real risk, and help you implement controls with minimal disruption.
Also look for evidence of solid cyber fundamentals (identity security, secure configuration, incident response) plus AI-specific capability. If they talk only about “AI magic” and not about access controls, logging, and failure modes—run.
One more UK-specific insight: the Cyber Security Breaches Survey 2025 estimates the average cost of the most disruptive breach at £1,600 (or £3,550 excluding £0 costs).[1] That’s a painful bill for most SMEs—and it rarely includes the hidden cost: trust.
If you’re building AI into operations and customer workflows, we can help you scope the work, set the guardrails, and avoid the classic “we moved fast and leaked data” moment.
An AI consultant focuses on outcomes (automation, productivity, use cases, implementation). An AI security consultant focuses on preventing harm (data leakage, unsafe automation, AI-specific attacks) and putting controls in place so you can use AI without breaking trust or compliance.
If AI tools will touch customer data, internal documents, finance/HR data, or trigger actions (emails, CRM updates, refunds, access changes), you should do an AI security review before launch—ideally while the workflow is still easy to change.
The big three are (1) accidental data leakage through prompts/uploads, (2) prompt injection and unsafe customer-facing bots, and (3) insecure connectors and permissions. Shadow AI and weak governance make all three worse.
Costs vary by scope. A focused assessment for a small number of tools/workflows is usually a fixed project; a wider programme (multiple systems, customer-facing AI, regulated data) is larger. Ask for a clear scope, deliverables, and a phased plan so you can start small and scale safely.
Keep it short: approved tools, banned behaviours (pasting confidential data, sharing credentials), rules for customer data, guidance on citations/accuracy, and escalation steps when something looks wrong. Here’s ours: AI Usage Policy.
It can. If personal data is processed by an AI tool, you still need lawful processing, purpose limitation, data minimisation, and appropriate security. The ICO’s UK GDPR guidance is a good starting point for principles and organisational responsibilities.[7]
Short list of reputable sources used for statistics and guidance (all links verified live on 28 December 2025).
Note: This article is for general information only and isn’t legal advice or a substitute for professional cyber security support. If you handle regulated or high-risk data, consult qualified security and legal professionals.