Artificial Intelligence & Machine Learning Security

Use AI with confidence. We test your models, LLM apps and data pipelines for prompt injection, data poisoning and leakage, and set the governance regulators expect.

Service Details

AI and ML security means you can put models and AI tools into production knowing they will not leak your data, follow an attacker’s instructions or fall foul of regulators. We test your large language model (LLM) applications, models and data pipelines, and set the governance that shows customers and auditors you are in control.

This service is part of our assessments and engineering work. It covers both the AI you build and the AI tools your staff already use, from a single chatbot to models trained on your own data.

INFO

You get a clear, tested view of your AI risks, and controls mapped to the frameworks your customers and regulators recognise.

What AI and ML security covers

Testing, supply chain and governance for the AI you build and use:

LLM and Model Testing

Hands-on testing for prompt injection, data leakage, excessive agency and unsafe output handling.

Model Supply Chain

Checks on third-party models, datasets and libraries, and on who can change training data.

AI Use and Data Exposure

A review of which AI tools staff use and what data reaches them, including Copilot oversharing.

AI Governance

Policies, an AI system register and risk assessments aligned to ISO/IEC 42001 and the NIST AI RMF.

Copilot exposure usually comes down to permissions, so our Microsoft 365 team can tighten sharing before or after rollout.

Who AI and ML security is for

You will get most from this service if you are building products on LLMs or your own models, or rolling out AI assistants to staff. It also suits firms whose customers now send AI questions in security questionnaires and expect evidence in return.

Regulated firms in financial services and insurance feel this first, because AI often touches credit, pricing or claims decisions. Private equity investors use it to check how a target governs AI before they buy.

The threats we test for

AI systems fail in ways traditional applications do not. Our testing follows the OWASP Top 10 for LLM Applications (2025 edition) and maps attacker techniques using MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems).

  • Prompt injection — Instructions hidden in user input, documents or web pages that make the model ignore its rules or leak data.
  • Data and model poisoning — Tampered training or fine-tuning data that changes how the model behaves, sometimes only for specific inputs.
  • Model supply chain — Pre-trained models, datasets and packages from public sources that carry hidden behaviour or vulnerabilities.
  • Sensitive information disclosure — Models and retrieval pipelines that return personal data, secrets or system prompts to the wrong user.
  • Excessive agency — AI agents with more access than they need, able to send emails, change records or run code on an attacker’s say-so.
An engineer reviewing AI system configuration across two bright office monitors

How we work

  • Scope — We list your AI systems and tools, who owns them and what data they touch, so testing focuses where the impact is highest.
  • Threat model — We map data flows, trust boundaries and the actions each model can take, using our threat modelling approach.
  • Test — We attack your models, prompts, retrieval sources and integrations in an agreed environment, without touching live customer data unless you approve it.
  • Fix and retest — We work with your engineers on fixes, then retest to confirm each issue is closed.
  • Govern — We set the policies, register and review cycle that keep controls current as models and regulations change.

What you receive

Each engagement ends with documents your engineers can act on and your auditors can read. They stay useful after we leave, because they describe your systems, not a generic checklist.

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Findings Report

Issues ranked by impact, with reproduction steps and fixes

AI System Register

Every model and AI tool, its owner, data and risk rating

Policy Set

Acceptable use and AI development policies for your teams

Risk Entries

AI risks scored and added to your existing risk register

Evidence for audits and regulators

Customers, auditors and regulators increasingly ask how you govern AI. The work above produces that evidence: test reports, risk assessments, an AI system register and policies with review dates.

We map it to the frameworks you answer to. These include ISO/IEC 42001 (the AI management system standard), the NIST AI Risk Management Framework, and the UK AI Cyber Security Code of Practice published by the Department for Science, Innovation and Technology (DSIT) in January 2025. If you serve EU customers, we also map it to your duties under the EU AI Act. Where AI processes personal data, we align with UK GDPR, and controls carry across to ISO 27001 so you are not maintaining two systems.

Services that work alongside

AI features are still software, so application security and CI/CD security cover the code and pipelines around your models. Penetration testing tests the infrastructure they run on, and cloud engineering secures the platforms hosting them.

For the risk and policy side, risk management keeps AI risks alongside the rest, and user awareness training shows staff what not to paste into AI tools.

Keep up with AI security

Our Insights page covers AI security, regulation and architecture for UK teams. Start with what AI security is, AI cyber security strategies for fintech and securing machine learning in financial services.

Visit our Insights page

Frequently asked questions

Put your AI on a secure footing

Tell us what you are building or the AI tools your staff already use. You get a clear view of where the real risks sit and what to fix first.

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