Reinforcement Learning Approaches for Adaptive Cybersecurity Defense Mechanisms

ABSTRACTRansomware has evolved from opportunistic malware into a mature criminal business model thatblends rapid encryption, lateral movement, and data extortion. Traditional signature-based andrule-driven defenses struggle to keep pace with fast-changing ransomware variants, adversarialevasion, and the operational complexity of modern digital environments. This paper proposes anartificial intelligence (AI)–driven approach to enhance ransomware detection and response byintegrating behavior-based analytics, anomaly detection, supervised classification, and decisionsupport automation within a governance-aligned incident response workflow. Building on thebroader role of AI in cybersecurity defense mechanisms, the study develops a conceptualframework that links technical detection and response capabilities to national cybersecuritystrategy principles, critical infrastructure protection priorities, and organizational culturereadiness. The proposed architecture emphasizes continuous learning, context-aware riskscoring, and response orchestration designed to reduce time-to-detect and time-to-contain whilemaintaining policy compliance and operational resilience. The paper concludes with anevaluation blueprint using defensible metrics and a strategic alignment checklist to support realworld deployment.