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.
- AI security is not a “nice to have”. It’s a prerequisite for using AI with customer data, internal docs, or customer-facing automation.
- The biggest SME risk is accidental data exposure. Leaky prompts, messy permissions, and shadow AI are more common than Hollywood-style hacking.
- Start small: map AI use cases, set an AI usage policy, lock down access, and monitor usage before you scale.
- Ask better questions when hiring: you want someone who understands both cyber security fundamentals and AI-specific threats and controls.
- Use trusted frameworks: UK guidance (AI Cyber Security Code of Practice), OWASP GenAI/LLM risks, and a practical risk framework (e.g., NIST AI RMF) help you move fast and stay sane.
- What an AI security consultant actually does (and what they don’t)
- Why AI security feels different to “normal” cyber security
- The AI security risks UK SMEs should prioritise first
- A minimum viable AI security programme (SME-friendly)
- What an AI security engagement looks like
- How to choose the right AI security consultant
- FAQs
- References
What an AI security consultant actually does (and what they don’t)
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:
- Data boundaries: what data goes into AI tools, where it’s stored, who can access it, and what gets logged.
- Threats unique to AI: prompt injection, data leakage, insecure plugins/integrations, model supply-chain issues, and misuse (intentional or accidental).
- Operational guardrails: access controls, usage policies, monitoring, incident response, and “kill switches” for customer-facing AI.
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.
Why AI security feels different to “normal” cyber security
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.
The AI security risks UK SMEs should prioritise first
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:
- Data leakage via prompts or uploads
Sensitive client details, contracts, HR info or pricing gets pasted into a chatbot—or ends up in logs, tickets, or shared outputs. - Prompt injection and malicious instructions
Attackers craft input that makes an AI system ignore instructions, reveal data, or take risky actions (especially with customer-facing chatbots or agents). - Insecure plugins, connectors and automations
“Helpful” integrations can become a shortcut into your data. This is where access controls and audit logs matter. - Shadow AI (unsanctioned tools)
People will use AI to save time. If you don’t provide safe options, staff will improvise with unsafe ones. - Weak governance
No named owner, no risk acceptance process, no policy, no monitoring. This is how “small experiments” become “enterprise risk”.
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]
A minimum viable AI security programme (SME-friendly)
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.
List every AI tool in use (official and unofficial), what it connects to, who uses it, and what data goes in/out. If you can’t answer “where does the data go?”, that’s a red flag.
Write a short, clear AI usage policy that spells out what staff can and can’t do (especially around customer data, credentials, and confidential documents). Then define a simple data classification: public / internal / confidential / restricted.
Enforce MFA, restrict admin rights, limit who can connect tools to core systems, and turn on audit logging. If a tool can call your CRM, inbox, or file store—treat it like a privileged user.
Run red-team style tests for customer-facing bots (prompt injection), review your “most sensitive” workflows, and set up monitoring for unusual access or output. Decide what triggers a shutdown.
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.
- AI ethics consultant — what “responsible AI” means in practice.
- AI strategy consulting — turning experimentation into a usable roadmap.
- AI consulting articles — our wider AI cluster if you’re building capability across the business.
What an AI security engagement looks like
Good engagements are structured, time-boxed, and end with something you can actually operate. Here’s what you should expect:
- Discovery & mapping: inventory of AI tools, workflows, data, integrations, owners.
- Risk assessment: threats, likelihood, impact, and “what would go wrong first”.
- Control design: access control, logging, red-teaming approach, incident playbooks, policy updates.
- Implementation support: enablement for internal IT/ops, vendor configuration, “safe defaults”.
- Executive read-out: decisions, trade-offs, priority fixes, budget estimate.
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.
How to choose the right AI security consultant
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.
- How will you map our AI data flows (inputs, outputs, logs, storage, retention)?
- How do you test for prompt injection and unsafe agent actions?
- What controls do you recommend for permissions, identity, and audit logging?
- How do you handle “shadow AI” and staff behaviour (policy + training)?
- What deliverables will we get (risk register, roadmap, policy templates, incident plan)?
- How do you align with UK guidance, including the AI Cyber Security Code of Practice?[4]
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.
FAQs
What’s the difference between an AI security consultant and an AI consultant?
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.
When should an SME bring in AI security support?
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.
What are the biggest AI security risks for UK SMEs?
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.
How much does an AI security consultant cost in the UK?
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.
What should be in an AI usage policy?
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.
Does using AI create GDPR / UK GDPR issues?
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]
References
Short list of reputable sources used for statistics and guidance (all links verified live on 28 December 2025).
- GOV.UK — Cyber security breaches survey 2025 (Updated 19 June 2025)
- IBM — Cost of a Data Breach Report 2025 (2025)
- Verizon — 2025 Data Breach Investigations Report (DBIR) Executive Summary (PDF) (2025)
- GOV.UK (DSIT) — AI Cyber Security Code of Practice (Published 31 January 2025)
- OWASP — OWASP Top 10 for LLM Applications 2025 (17 November 2024)
- NIST — AI Risk Management Framework (AI RMF 1.0) (PDF) (January 2023)
- Information Commissioner’s Office (ICO) — UK GDPR: Data protection principles (Accessed 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.