  Cyber attacks pose existential threats to nations and enterprises. Current
practice favors piece-wise analysis using threat-models in the stead of
rigorous cyber terrain analysis and intelligence preparation of the
battlefield. Automated penetration testing using reinforcement learning offers
a new and promising approach for developing methodologies that are driven by
network structure and cyber terrain, that can be later interpreted in terms of
threat-models, but that are principally network-driven analyses. This paper
presents a novel method for crown jewel analysis termed CJA-RL that uses
reinforcement learning to identify key terrain and avenues of approach for
exploiting crown jewels. In our experiment, CJA-RL identified ideal entry
points, choke points, and pivots for exploiting a network with multiple crown
jewels, exemplifying how CJA-RL and reinforcement learning for penetration
testing generally can benefit computer network operations workflows.
