Explainable AI for Secure and Reliable Cyber-Physical System Operations

Abstract

Cyber-Physical Systems (CPS) are increasingly integrated into critical infrastructures, including healthcare, smart grids, and industrial automation, requiring high reliability, security, and interpretability. However, traditional AI models used for CPS operations often function as black-box systems, lacking transparency and explainability, which raises concerns regarding trust and accountability. This paper explores the role of Explainable AI (XAI) in enhancing the security and reliability of CPS operations, providing human-interpretable insights into model decisions. We propose a novel XAI-driven security framework that integrates graph-based explainability, attention mechanisms, and causality analysis to detect anomalies, predict failures, and mitigate cyber threats in real-time. The proposed approach is evaluated using CPS datasets across multiple domains, demonstrating its effectiveness in improving system resilience, reducing false positives in security monitoring by 35%, and enhancing real-time decisionmaking accuracy. Our findings emphasize that integrating XAI techniques into CPS security protocols can significantly enhance situational awareness, compliance with regulatory standards, and proactive threat mitigation. This research highlights the need for further advancements in interpretable AI methodologies to establish more transparent, robust, and accountable CPS frameworks.