AI-Driven Security Operations and Anomaly-Centric Threat Investigation: Integrating SOC Playbooks, Insider Threat Analytics, and Zero Trust Paradigms

Abstract

The accelerating sophistication of cyber threats has fundamentally reshaped the operational realities of contemporary Security Operations Centers, compelling a paradigm shift from reactive alert handling toward intelligence-driven, adaptive, and automation-supported investigative practices. Among the most disruptive and costly threats, ransomware campaigns and insider-enabled security breaches stand out due to their hybrid technical and behavioral characteristics, long dwell times, and capacity to evade signature-based detection mechanisms. This research article develops a comprehensive, theory-driven examination of how artificial intelligence–enabled Security Operations Center playbooks can be systematically integrated with anomaly detection methodologies, insider threat analytics, and Zero Trust security principles to enhance investigative rigor and operational resilience. Drawing exclusively on the provided scholarly and institutional references, the article synthesizes decades of research spanning knowledge-based intrusion detection, graph-centric behavioral modeling, deep learning–driven anomaly detection, and modern SOC orchestration frameworks. Particular emphasis is placed on the conceptual and operational contributions of AI-optimized investigative playbooks for ransomware incidents, as articulated in recent literature, and on their capacity to unify heterogeneous data sources, automate hypothesis generation, and support analyst decision-making under conditions of uncertainty. Methodologically, the study adopts an interpretive and integrative research design, leveraging comparative literature analysis to derive a unified conceptual framework that bridges network-level anomalies, host-based behavioral deviations, and organizational trust assumptions. The results section articulates a descriptive synthesis of emergent patterns across the literature, demonstrating how AI-driven SOC playbooks function as socio-technical control mechanisms that encode institutional knowledge, align detection and response workflows, and mitigate cognitive overload among analysts. The discussion extends these findings by situating them within broader theoretical debates concerning automation bias, explainability, and the ethical governance of behavioral surveillance in organizational contexts. The article concludes by outlining a forward-looking research agenda focused on adaptive trust modeling, cross-domain anomaly fusion, and the institutionalization of AI governance within security operations. By offering an extensive, publication-ready analysis, this work contributes to the maturation of cybersecurity as an interdisciplinary field that integrates machine intelligence, human judgment, and organizational strategy.