Explainable Artificial Intelligence Techniques for Enhancing Trust in Predictive Security Models
B . Lakshmi Dhevi -, T. R. Vedhavathy, Mohammed Muzaffar Hussain · 2025
Explainable Artificial Intelligence (XAI) has emerged as a crucial component in enhancing the transparency, trust, and effectiveness of predictive security models. As cybersecurity threats become increasingly sophisticated, the need for interpretable models that ensure reliable decision-making in real-time has never been greater. This chapter explores the integration of XAI techniques in security systems, focusing on their role in improving model interpretability without compromising predictive accuracy. The challenges of balancing computational efficiency with high-quality explanations in real-time anomaly detection systems are discussed, alongside the importance of feature importance analysis for understanding model behavior. Model-agnostic approaches such as SHAP and LIME are examined for their ability to provide insights into complex, black-box models while maintaining high levels of accuracy. The chapter further investigates the evolving landscape of cybersecurity, where dynamic and novel threats require continuous adaptation of anomaly detection systems. It highlights the practical implications of XAI in fostering trust and providing actionable insights for security professionals, ensuring that security measures are both robust and understandable. The chapter concludes by identifying future directions for XAI research in predictive security models, emphasizing the need for scalable, efficient, and secure explainability solutions.