An ever-expanding cyber-attack surface, infrequent computer vulnerability scans, and burdensome security procedures create a seemingly lopsided battle when it comes to defending critical computing assets. Couple those factors with costly cybersecurity assessments that often lack actionable feedback, and the odds may appear to favor bad actors.
DARPA intends to change that dynamic through a new program focused on technology that can accelerate cybersecurity assessments with automated, repeatable, and measurable approaches.
The Cyber Agents for Security Testing and Learning Environments (CASTLE) program seeks to improve cyber testing and evaluation by developing a toolkit that instantiates realistic network environments and trains AI agents to defend against advanced persistent cyber threats (APTs). Teams will use a class of machine learning known as reinforcement learning to automate the process of reducing vulnerabilities within a network.