A structured evaluation of an AI system before it reaches production. We map the deployment across various AI layers — how it's built and how it behaves — surface the real gaps, and attach the specific controls that close them. Pre-deployment, not post-breach.
A design-level review of how the AI system is actually engineered — data flows, model integration, tooling and orchestration, agent behavior, and memory. We find the weaknesses built into the architecture before they convert into production risk.
Adversarial testing of live web applications and their AI surfaces. We attack the system the way a real adversary would — auth, business logic, injection, and AI-specific attack paths — to prove what holds and what doesn't.
Target-state security architecture for systems and AI deployments — designed so security is built in, not bolted on. Identity, segmentation, trust boundaries, logging, and control placement defined before build.