Rethinking ransomware defense in the age of generative AI

Ransomware remains one of the most disruptive cyber threats, with global damages projected to exceed USD 265 billion annually by 2031. Modern campaigns increasingly involve human-operated tactics, double-extortion techniques, and rapidly evolving malware variants, making traditional signature-based and purely discriminative detection approaches insufficient. As adversaries adopt more sophisticated strategies, there is a growing need for adaptive defense models that can anticipate novel behaviors and support reliable operational decision-making. This paper proposes a Generative AI–driven conceptual framework for strengthening ransomware defense by integrating (1) synthetic data generation and behavioral forecasting, (2) adversarial behavior simulation for stress testing and continuous learning, and (3) explainable AI mechanisms that provide analyst-interpretable rationales to support trust calibration and human-AI collaboration in security operations centers. The contribution is conceptual: rather than introducing new standalone algorithms, the framework explicitly formalizes a closed-loop triadic integration linking generative threat modeling, explanation-driven decision support, and resilience-oriented governance. This positioning clarifies what is novel about GenAI-RD compared with prior adaptive defense models that treat these dimensions in isolation. By synthesizing current ransomware trends, Generative AI advances, and security analytics research, the framework illustrates how generative techniques can shift defense from reactive detection toward proactive, transparent, and trust-centered resilience. The paper further outlines feasibility and risk considerations—including data quality constraints, computational overhead, and exposure to data poisoning—to guide realistic adoption. The framework offers actionable guidance for cybersecurity practitioners, incident response teams, and policymakers seeking to operationalize responsible and explainable AI-based defenses in high-risk digital environments.