The accelerating complexity of cyber threats has fundamentally altered the operational, analytical, and strategic requirements of contemporary cybersecurity ecosystems. Among these threats, ransomware has emerged as a particularly disruptive and adaptive phenomenon, intertwining technical exploitation with psychological coercion, organizational pressure, and economic extortion. Parallel to this development, insider threats, advanced persistent threats, and large-scale network intrusions have converged into a multifaceted risk landscape that challenges traditional rule-based and signature-driven defense mechanisms. This article develops a comprehensive, publication-ready research framework that integrates artificial intelligence–driven security operations center optimization, anomaly detection, topic modeling, graph-based behavioral analysis, and deep learning architectures into a unified analytical paradigm for advanced cyber threat detection and ransomware investigation. Grounded strictly in the provided scholarly references, the study positions AI-optimized SOC playbooks as an epistemic and operational bridge between reactive incident response and proactive threat intelligence, with particular emphasis on the ransomware investigation lifecycle as articulated by Rajgopal (2025).

The article advances three interlocking contributions. First, it reconstructs the theoretical lineage of cyber threat detection, tracing its evolution from statistical outlier analysis and pattern classification to contemporary deep learning and graph-based behavioral analytics. Second, it proposes a text-based methodological synthesis that conceptually integrates latent topic modeling, kernel-based learning, novelty detection, and user behavior analytics into SOC workflows without reliance on visual or mathematical formalism. Third, it delivers an interpretive results and discussion narrative that situates empirical-style findings within broader scholarly debates on explainability, scalability, class imbalance, and adversarial adaptation. Throughout the paper, ransomware is treated not merely as malware but as a socio-technical process embedded within organizational, psychological, and networked contexts.

By emphasizing theoretical elaboration, critical comparison, and interpretive depth, this work addresses a persistent literature gap: the absence of holistic, AI-driven investigative frameworks that unify ransomware response with insider threat detection and large-scale network analytics. The findings underscore that AI-optimized SOC playbooks, when grounded in rigorous data science principles and contextual awareness, can significantly enhance detection fidelity, investigative coherence, and strategic resilience against evolving cyber threats (Rajgopal, 2025; Chandola et al., 2009; Sommer & Paxson, 2010).