AI that learns to defend
Protection that evolves, adapts and stays ahead.
The challenge
Traditional network defense relies on rules written in advance. Attackers adapt faster than those rules can be updated.
Our approach
We train AI agents in realistic cyber ranges. Attacker agents chase specific objectives. Defender agents analyze security data, contain threats and keep critical services running. A unified message bus connects both sides to high-speed simulations for training at scale, and to fully emulated networks of real machines for validation.
Highlights
- Offensive and defensive AI agents that train against each other
- Training at scale in simulation, validation on real operating systems, security tools and attack techniques
- A path toward autonomous cyber defense grounded in real-world conditions
Built on
Simulation-to-emulation bridge