Your teams are already using AI.
Do you know if it’s secure?
Teams are deploying LLMs, building agents, utilizing data and integrating AI into workflows without security review or regulatory awareness.
We help you govern, test, and secure AI initiatives before they become your next compliance or data protection incident.
For CTO, CISO, DPO, Head of Compliance
AI is moving faster than your governance.
That’s a regulatory problem.
Every department is experimenting with AI. Customer service has a chatbot. Finance is using copilots. Product teams are integrating LLMs into workflows. Most of this is happening without security review, data classification, or governance.
The EU AI Act is law. GDPR already applies to AI processing. NIS2 includes AI systems in scope. If you are developing, deploying, or procuring AI solutions, you need governance, testing, and evidence. Not just an acceptable use policy.
Most organisations don’t need another AI playbook. They need someone to test their LLMs for vulnerabilities, build the governance framework, and produce the evidence that regulators and auditors will accept.
End-to-end AI security. From architecture review to red team.
Secure AI Development Consulting
We help your teams build AI systems with security embedded from day one. Secure coding patterns, privacy by design, safe data handling, and guardrails for model use. The goal: ship features faster without opening new attack paths.
LLM Red Teaming & Penetration Testing
We simulate real attackers against your LLMs, agents, and prompts. Jailbreaks, data leakage, prompt injection, tool misuse. You get a prioritised findings report, reproducible exploits, and clear fixes that harden both the model layer and the surrounding application.
LLM Security Upskilling Workshops
Your engineers are building with AI but most have never seen an LLM attack up close. We run hands-on sessions covering real exploits, defensive patterns, and threat modelling so your team makes secure design decisions by default, not after the next incident.
Review of AI Architecture
Before you scale, get a second opinion on your AI design. We assess model selection, data flows, safety controls, and failure scenarios. You get specific recommendations that reduce cost and risk before they compound.
AI Threat Modelling & Risk Assessments
We identify how your AI can fail or be abused, score impact and likelihood, and define mitigations tied to owners and timelines. You walk away with a living risk register, testable controls, and metrics to prove improvements over time.
Ongoing AI Security Consulting
A long-term partnership that tracks evolving threats, reviews new features, and keeps your security posture current. We join design reviews, advise on vendor selection, and provide rapid response when something looks off.
Your teams are using ChatGPT and copilots but nobody has reviewed the data flows.
You're deploying an LLM-powered product and need to know if it's secure before launch.
A client or regulator asked about your AI governance and you didn't have a clear answer.
You're subject to the EU AI Act but haven't started compliance work.
Your AI development pipeline has no security testing or review process.
You want to scale AI initiatives but need a governance framework first.
Frequently Asked Questions
AI security protects the AI systems your organisation builds and uses — LLMs, agents, copilots, and the data flowing through them — against attacks that traditional security tooling does not catch: prompt injection, jailbreaks, data leakage, and tool misuse. It matters now because most teams are already deploying AI without security review, creating an attack surface that firewalls and endpoint protection were never designed to cover.
LLM red teaming is controlled, simulated attack testing of your AI systems — models, agents, and prompts — before real attackers try the same techniques. Epity tests for jailbreaks, prompt injection, data leakage, and tool misuse, then delivers a prioritised findings report with reproducible exploits and clear fixes. It is the AI equivalent of penetration testing, and the fastest way to learn whether an AI product is safe to launch.
Unreviewed data flows are the biggest one: confidential client data, source code, or credentials pasted into external tools without anyone assessing where that data goes. Add shadow AI — tools adopted without approval — and you have exposure no policy document alone will fix. The pragmatic response is not a ban; it is an assessment of real usage, guardrails for data handling, and training that teaches teams secure patterns.
Yes. High-risk AI systems must meet requirements for accuracy, robustness, and cybersecurity, with documented risk management and human oversight. Following the EU's June 2026 simplification package, those high-risk obligations apply from December 2027 at the earliest — but transparency rules take effect in August 2026, and prohibitions have applied since February 2025. Waiting is a poor strategy: an AI inventory, risk assessment, and security testing take months to do properly.
Before, every time. A pre-launch architecture review examines model selection, data flows, safety controls, and failure scenarios while changes are still cheap to make. Threat modelling then produces a living risk register with testable controls, owners, and timelines. Red teaming just before release validates the result. Retrofitting security after an incident — or after a regulator's question — costs far more than designing it in.
Yes. When your teams feed company data into a vendor's model, the data flows, access controls, and misuse potential are your risk regardless of who built the system, and clients and regulators will direct their questions to you. Sensible steps: an inventory of AI tools in actual use, a review of what data reaches them, vendor selection criteria, and a governance framework before usage scales further.
Let’s talk about your AI security posture.
One of our founding partners will review your request soon.