AI security · UK · 2026

AI security audit cost in the UK (2026)

A typical UK AI security audit costs £6,500 to £15,000 in 2026 — a one-off red-team assessment of an LLM or AI feature. A simple hosted-chatbot check can start near £5,000; a full adversarial engagement against a multi-agent or in-house model can run to £75,000 or more. The spread is wide because “AI security audit” covers everything from a half-week probe to a months-long programme.

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How much does an AI security audit cost in the UK? For most companies testing a single LLM-powered product, the honest range is £6,500 to £15,000 for a one-off engagement. A light assessment of a chatbot built on a third-party model, with limited integration, can start around £5,000. At the other end, a comprehensive test of an agentic system or a model you have trained yourself — with full adversarial testing and sensitive data in scope — sits well into the tens of thousands, sometimes £75,000 or more. Ongoing programmes that re-test as your prompts and models change are usually priced annually, in the £30,000 to £120,000 region.

An AI security audit is a structured attempt to break your AI system before someone else does. It goes past a normal application penetration test to probe the things that are specific to models — prompt injection, jailbreaks, training or retrieval-data poisoning, sensitive-data leakage through outputs, and an agent being talked into doing something it should not. Below we set out what the tiers cost, what actually moves the price, how an audit differs from a standard pen test, and how to scope one without paying for depth you do not need.

What an AI security audit costs by scope (2026)

Engagement type	Typical UK cost	Roughly what it covers
Single chatbot / LLM feature audit	£5,000–£8,000	One-off test of a hosted-model chatbot with limited backend integration
LLM application or RAG system audit	£8,000–£20,000	Prompt injection, retrieval poisoning, data leakage and the surrounding logic
Multi-agent / proprietary-model engagement	£20,000–£75,000	Full adversarial testing of agentic systems and models you have trained
Continuous AI security programme (annual)	£30,000–£120,000	Repeat testing as prompts and models change, plus regression checks
Specialist day rate (for comparison)	£1,000–£2,000 / day	Blended AI and security expertise, charged by the day rather than fixed-scope

Sources: PenTest Testing AI penetration testing cost guide 2026; Cyphere, Fortbridge, Precursor Security, EJN Labs and Intruder (UK penetration testing pricing); Repello AI red-teaming pricing; ITJobsWatch (UK AI and security contractor rates) · Indicative ranges, updated September 2026

Those bands overlap, and they should — a genuinely tricky RAG system can cost more to test properly than a small agentic prototype. The number on a quote tracks the days of skilled effort involved far more closely than it tracks the label someone has put on the engagement. Read the scope, not the title.

What moves the price

Two audits with the same headline figure can mean very different amounts of work. AI security testing is priced on skilled days, and a handful of things decide how many of those you are buying.

What you are actually testing

A single chatbot on a hosted model is a small surface. An agent that can call tools, touch a database and act on its own outputs is a much larger one — every capability you give it is another way in.

Your own model or someone else’s

Testing a third-party model you call over an API is narrower than testing weights you trained yourself. Owning the model adds data-poisoning, extraction and fine-tuning attacks that simply do not apply when the model sits behind someone else’s API.

How much access the tester gets

A black-box test — poking at the system from outside with no inside knowledge — is cheaper but shallower. A white-box test, where the tester sees your prompts, code and retrieval setup, costs more and finds far more. Most serious audits sit somewhere in between.

Regulated or sensitive data in scope

If the system touches personal, financial or health data, testing has to prove that data cannot leak through the model’s outputs or logs. That raises both the depth required and the care taken over evidence, and it shows up in the price.

One-off or ongoing

Models drift, prompts get rewritten and new features open new holes, so a point-in-time audit ages quickly. A programme that re-tests on a schedule costs more up front but is usually the cheaper way to stay safe over a year.

Audit, penetration test or continuous programme?

These three overlap in people’s heads and are priced quite differently. Knowing which one you actually need is the quickest way to avoid over- or under-buying.

One-off AI security audit

A fixed-scope assessment of a specific AI feature, delivered as a report with findings and fixes. Right when you are about to launch, or when a client or regulator wants assurance. It is a snapshot — accurate on the day, and only that day.

Extended penetration test

A traditional application pen test stretched to cover the model layer. Sensible when the AI is a small part of a wider system you are already testing. Cheaper, but the AI-specific coverage is only as deep as the tester’s model experience.

Continuous red-teaming

Ongoing testing that re-runs as your prompts, models and data change, often with automated checks between manual rounds. The fit for anything high-stakes or fast-moving — it costs more, but nothing else keeps pace with a system that ships weekly.

Many teams start with a one-off audit before launch, then move to a lighter continuous arrangement once the system is live and changing. If you are earlier than that and unsure whether your setup is even ready to test, an AI readiness assessment is the cheaper first step. Keeping the live system watched afterwards — for cost, accuracy and abuse — is a job for ongoing monitoring rather than a security audit.

How to scope an audit well

The audits that give good value share a shape: a clear reason for doing them, a scope that matches the real risk, and a report you can actually act on. A few things worth insisting on before you sign.

Name the system and the data

Say exactly which feature is in scope and what data it can reach. A vague brief either balloons in cost or quietly leaves the risky part untested — usually the second.

Ask for the methodology

A credible tester will map to recognised guidance such as the OWASP Top 10 for LLM applications and the NCSC’s AI security guidance. If nobody can tell you what framework they work to, that is your answer.

Insist on fixes, not just findings

A list of vulnerabilities with no guidance on how to close them is half a job. The value is in remediation advice a developer can act on, and a retest to confirm the holes are actually shut.

Right-size the depth

A pre-launch consumer chatbot does not need the treatment a bank’s agentic system does. Match the rigour to what the system can actually do and what it would cost you if it failed.

Red flags when an audit looks cheap

A low number is not automatically a saving. A handful of things are worth checking before you commit.

A scan dressed up as an audit

Automated tooling has its place, but a script pointed at your endpoint is not an AI security audit. Real testing needs a human thinking adversarially about how your specific system can be misused.

No AI-specific experience

A general pen-test firm with no track record in models will test the web app around the AI and miss prompt injection, jailbreaks and data leakage entirely. Ask what LLM work they have actually done.

No retest included

If the quote ends at the report and charges again to confirm your fixes worked, the cheap headline can cost more once you add the second visit. Get the retest written in.

We build and operate our own AI products in regulated and consumer-facing sectors, so we test the way we build — senior people, honest about what a system can and cannot withstand, and clear about the difference between a finding that matters and one that just fills a page. For more on how AI work is priced and bought in the UK, browse the rest of our AI insights, or read how we approach secure AI development in the first place.

FAQ

AI security audit cost: common questions

A typical one-off AI security audit costs £6,500 to £15,000 in 2026 for a single LLM-powered product. A light assessment of a hosted-model chatbot can start around £5,000, while a full adversarial engagement against a multi-agent or self-trained model can reach £75,000 or more. Ongoing programmes that re-test as your system changes are usually priced annually, roughly £30,000 to £120,000.

An AI security audit, sometimes called AI red teaming, is a structured attempt to break your AI system before an attacker does. It tests the things that are specific to models — prompt injection, jailbreaks, training or retrieval-data poisoning, sensitive-data leakage through outputs, and agents being manipulated into unsafe actions — on top of the normal application security checks. The output is a report of findings, their severity and how to fix them.

It needs two skill sets in one person or team — security testing and a working understanding of how models behave — which is scarcer and priced accordingly, often £1,000 to £2,000 a day. Models also fail in fuzzier ways than ordinary software: a prompt injection or a jailbreak is not a fixed exploit but something a tester has to probe for creatively. That exploratory work takes time, and time is what you are paying for.

A focused audit of a single chatbot or LLM feature usually takes a few days to about a week of testing, plus a little time to write up. A larger engagement covering an agentic system, your own model or sensitive data can run two to four weeks or longer. Continuous programmes are not a single block at all — they run in shorter rounds across the year as your system changes.

The AI-specific checks usually include prompt injection and jailbreaks, sensitive-data leakage through model outputs, training or retrieval-data poisoning, model or system-prompt extraction, and — for agents — whether the system can be talked into unsafe actions like calling the wrong tool or reaching data it should not. Good testers map this to recognised guidance such as the OWASP Top 10 for LLM applications and the NCSC’s AI security guidance, alongside the standard application security tests around the model.

Often yes, though the audit is narrower and cheaper. You are not responsible for the model provider’s internals, but you are responsible for how you use it — the prompts you send, the data you expose to it, and what you let its output trigger in your own systems. Prompt injection and data leakage are still very much your problem even when someone else trained the model, so the integration around it is worth testing.

At minimum before launch and after any significant change — a new model version, a reworked prompt, or a new capability given to an agent. Because AI systems tend to change far more often than traditional software, many teams move to continuous or quarterly testing rather than a single annual audit. The right cadence follows how fast your system changes and how much damage a failure would do.

A one-off audit is fine for a stable system or a point-in-time assurance need, such as satisfying a client or a regulator before launch. If your AI ships often or handles anything high-stakes, a single audit goes stale quickly and continuous testing earns its keep. A common pattern is a thorough audit before launch, then a lighter ongoing arrangement once the system is live and evolving.

Need an AI system tested properly?

Tell us what you have built and what it can reach, and we will help you size the right audit — one-off or ongoing — and say plainly if a lighter check would cover your risk.

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