Explainable AI in Emerging Cybersecurity Domains

Abstract

Artificial Intelligence (AI) has become essential in cybersecurity systems, enabling threat detection, malware analysis, intrusion detection, phishing identification, and automated incident response. However, AI-driven models operate as black-box systems, limiting transparency, interpretability, accountability, and trust. These concerns become significant in domains such as Internet of Things (IoT),Industrial IoT(IIoT),cloud and edge computing, cyber-physical systems, smart healthcare infrastructures,blockchain ecosystems,autonomous transportation, and next-generation networks. This chapter outlines Explainable Artificial Intelligence (XAI) techniques and their role in enhancing trustworthy,responsible cybersecurity systems.It examines approaches including SHAP,LIME,saliency visualization, attention mechanisms, rule-based explanations, and counterfactual reasoning in cybersecurity applications. Furthermore, it explores integrating explainable AI into intrusion detection systems,malware analysis, cyber threat intelligence, adversarial attack detection, nd security operations centers.