Unveiling the black box: A multi-layer framework for explaining reinforcement learning-based cyber agents

This research paper presents a framework for understanding how reinforcement learning-based cyber agents (AI systems trained to make decisions by trial and error in cybersecurity contexts) make their decisions. The authors developed a multi-layer approach to explain the "black box" problem (the difficulty in understanding why AI systems reach certain conclusions), which is important for security experts to verify that these AI agents are operating correctly and safely.