AI-Driven Cyber Risk Management: A Review of Threat Detection, Prediction, and Mitigation Strategies

Abstract

As cyberattacks grow more frequent, automated, and difficult to detect using signature-based defenses, artificial intelligence (AI) has become a central pillar of cyber risk management across three interlocking functions: threat detection, risk prediction, and mitigation. This review synthesizes evidence on AI-driven approaches to each function, with particular attention to deep learning-based intrusion detection and the growing role of explainable AI (XAI) in making these systems trustworthy enough for operational deployment. Systematic evidence shows machine learning (ML) techniques — including convolutional neural networks, support vector machines, and Bayesian classifiers — substantially outperform manual, human-driven threat analysis (Alshuaibi et al., 2025), while quantitative research demonstrates that supply-chain network features measurably improve prediction of enterprise data-breach likelihood (Hu et al., 2022). A growing body of XAI research — spanning SHAP- and LIME-based interpretability methods for network intrusion detection systems and dedicated reviews of explainable deep learning for advanced persistent threat (APT) detection — addresses the interpretability gap that has historically limited analyst trust in black-box detection models (Abdul Mutalib et al., 2024; Rjoub et al., 2023). The review also examines the enterprise-architecture and infrastructure layer required to operationalize these capabilities at scale, including real-time event-driven data processing (Sannidhanam, 2021; Event Streaming Architectures for High-Volume Transaction Processing, 2022), configurable and metadata-driven enterprise platforms for embedding mitigation logic into operational workflows (Basireddy, 2022a, 2022b), audit-ready compliance architecture (Basireddy, 2023), and autonomous AI agents for continuous infrastructure remediation (Sannidhanam, 2025). The review concludes that closing the gap between AI’s detection and prediction capabilities and its practical, governed deployment increasingly depends on explainability research and enterprise-architecture maturity in equal measure.