The situation
More and more organizations are integrating advanced AI models into their core processes. While these applications are often treated as low-risk, they introduce new attack surfaces, data risks, and dependencies that aren't always visible within existing security and governance structures.
This early stage of adoption is exactly the right moment to implement AI systems that are secure, controllable, and robust from the start. Mitigating problems after the fact is demonstrably more complex and more expensive than security-by-design.
This requires a new approach to security: AI security, covering risk assessment, model behavior evaluation, and continuous monitoring of AI systems within the broader IT and security architecture.
The earlier you get a clear picture, the cheaper it is to act on.
What we can deliver
Risk assessment
A structured analysis of your AI application against a defined risk taxonomy, covering security threats, reliability failures, safety risks, and organizational exposure. The output is a prioritized risk register and a concrete list of recommended actions. This is a strategic picture, relevant for both technical teams and leadership.
Threat modelling
A technical assessment of how your system could be attacked. We map the attack surface and identify exploitable vectors. The output is a threat model your engineering team can work with.
Red teaming
We act as the attacker. Using real techniques on your actual system, we find what holds up and what doesn't.
Good fit if …
You're deploying AI and haven't done a structured risk or threat assessment yet.
Your security team has flagged AI as a gap but doesn't know where to start.
You're about to ship an AI product and want to know what can be exploited before users find it.
You need to demonstrate the board, investor or regulator that AI risks are identified and managed.
You've seen unexpected behaviour in your AI system and want to understand why.
