Explainable artificial intelligence in threat detection
Khushi Wadhwa, Himanshi Babbar, Shalli Rani · 2025
This chapter explores the pivotal role of explainable artificial intelligence (XAI) in enhancing threat detection across security-critical domains such as cybersecurity and national safety. While machine learning (ML) models have demonstrated remarkable capabilities in identifying threats, their opaque nature often hinders trust and effective human oversight. XAI bridges this gap by providing human-interpretable insights into complex model decisions, enabling analysts to understand, validate, and refine automated detections. The chapter discusses various XAI techniques, their architecture, and the significance of interpretability for fostering human-machine collaboration, mitigating biases, and conducting thorough error analysis. It also presents practical illustrations using interpretable models like linear regression and decision trees, demonstrating how feature importance and effect plots can elucidate model behavior. By emphasizing transparency and accountability, this chapter underscores the necessity of XAI for building robust and trustworthy threat detection systems.