AI Security & Cybersecurity
“I’m reluctant to deploy AI because I can’t control the risks.”
Deploying AI without controlling the risks isn’t an option in demanding environments. We build in security (conventional cybersecurity and AI-specific security) from the design stage, so you can deploy your AI projects with confidence.
Security built in from the start, not added later.
We treat conventional cybersecurity and AI-specific risks (prompt injection, data leakage, model drift) as a prerequisite, not an option.
- AI model red teaming: attack and robustness testing.
- Protection against prompt injection and sensitive data leakage.
- Cybersecurity for industrial and other demanding environments.
- Governance, auditability and regulatory compliance for AI systems.
The kind of projects we deliver.
Robustness audit of an AI chatbot
Robustness audit (red teaming) of an AI chatbot before go-live, with the vulnerabilities found then fixed.
Safeguards against prompt injection
Putting safeguards against prompt injection in place on an AI agent exposed to external users.
AI governance framework
Governance and auditability framework for AI models deployed in a regulated environment.
Examples of the kind of projects our teams deliver.
Focused on results
A pragmatic approach built around real results and efficiency, not technology for show.
Scoping first
We define the project before we talk about tools, so we avoid dead ends.
Real-world delivery
We take solutions all the way to production, not just to prototype.
Plain speaking
Complex topics made clear and practical, with no needless jargon and no overpromising.
The questions we hear most often.
What is red teaming applied to AI?
Actively testing an AI model by simulating real attacks (prompt injection, data extraction, bypassing safeguards) to find its vulnerabilities before a malicious third party does.
What is prompt injection and why is it dangerous?
A technique that manipulates the instructions given to a language model so that it ignores its safety rules or reveals sensitive data. We put specific safeguards in place to protect you.
Is industrial cybersecurity different from conventional IT cybersecurity?
Yes. Industrial environments (OT, SCADA) have availability and safety constraints that differ from conventional IT, where a poorly tested patch can stop a production line. We adapt our approach to these environments.
How do you demonstrate that our AI systems comply with regulations?
Through a documented governance and auditability framework: traceability of model decisions, access control and version history, to meet the requirements of your internal or external auditors.
Can AI be deployed securely without in-house cyber expertise?
Yes, that’s exactly what this support is for: we bring the AI cybersecurity expertise your teams may not have in-house, without you needing to hire for it before the project starts.
Have an AI project to secure?
Tell us about your context. We’ll get back to you shortly to assess the risks and the framework required.
