Secure and Explainable AI for Cyber Defense

Abstract

ABSTRACT Artificial Intelligence (AI) has significantly transformed cyber defense by enabling automated threat detection, malware classification, anomaly detection, and real time incident response. However, the integration of AI into cybersecurity infrastructures introduces two major challenges: security of AI models themselves and lack of explainability in complex machine learning systems. Adversarial attacks, model poisoning, data leakage, and model inversion threaten AI-driven security systems. At the same time, black box models such as deep neural networks make it difficult for analysts to interpret decisions, which affects trust, compliance, and accountability. This paper presents a comprehensive review of Secure and Explainable AI (SXAI) in cyber defense. It discusses security risks in AI models, defense mechanisms, explainability techniques, regulatory implications, and future research directions. The study also highlights practical frameworks and real-world applications where secure and interpretable AI can enhance resilience against evolving cyber threats.

KEYWORDS: Secure AI, Explainable AI, Cyber Defense, Adversarial Attacks, Model Poisoning, XAI, Intrusion Detection Systems, Trustworthy AI