Explainable AI Frameworks for Trustworthy Autonomous Cyber Defense System

The increasing sophistication and frequency of cyber threats necessitate a shift towards Autonomous Cyber Defense Systems (ACDS). While Artificial Intelligence (AI), particularly machine learning (ML), provides the requisite speed and scalability for such systems, their inherent opacity poses a significant barrier to trust and adoption. Unexplainable ACDS can lead to erroneous actions that are difficult to diagnose, hinder human-AI collaboration, and raise serious accountability and compliance concerns. This research article investigates the integration of Explainable AI (XAI) frameworks as a critical enabler for trustworthy ACDS. Through a mixed-methods approach combining a systematic review of XAI techniques with a quantitative case study on an intrusion detection dataset, this study evaluates the efficacy, performance trade-offs, and human-interpretability of prominent XAI frameworks in a cyber defense context. Findings indicate that post-hoc explanation methods, such as SHAP and LIME, are currently most practical for elucidating complex model decisions, but they introduce computational overhead. The study further reveals a tension between model interpretability and predictive performance, particularly for sophisticated ensemble and deep learning models. The discussion synthesizes a proposed hybrid XAI framework tailored for ACDS, balancing real-time explainability with defense performance. We conclude that for ACDS to be operationally trusted, XAI is not an optional add-on but a foundational requirement. The article outlines a roadmap for future research, emphasizing the need for standardized evaluation metrics, human-in-the-loop validation, and regulatory frameworks for explainable cyber operations.