REINFORCEMENT LEARNING WITH TRANSPARENT POLICIES AN EXPLAINABLE AI APPROACH TO ADAPTIVE CYBER SECURITY
Keywords:
Reinforcement Learning (RL), Transparent Policies, Explainable Artificial Intelligence (XAI), Adaptive Cybersecurity, Threat Detection, Decision Transparency, Cyber Threat MitigationAbstract
Cybersecurity solutions that are both intuitive and adaptable are becoming more important as the complexity of cyberthreats rises. Reinforcement learning (RL) systems choose the optimal action by analyzing their interactions with their surroundings, suggesting that they might be a good tactic for identifying potential dangers before they materialize. Normal reinforcement learning models are not necessarily suitable in critical security situations because they are difficult to interpret, often functioning as “black boxes.” As a result, this research aims to determine how RL systems might benefit from explainable AI (XAI) in adaptive cybersecurity. To guarantee the prompt identification of threats and to promote adherence to security regulations and human monitoring, the proposed method employs easily comprehensible policy models that clearly explain each decision. Our experimental results show that transparent reinforcement learning rules improve understanding of system behavior and enable rapid responses to emerging cyberthreats. By integrating explainable AI with reinforcement learning, the research outlines a path toward next-generation cybersecurity systems that are more reliable, accountable, and trustworthy.
