Ransomware has emerged as one of the most damaging cyber threats affecting modern digital systems. These attacks restrict access to critical data by encrypting files and demanding payment in exchange for decryption. Individuals, organizations, and even national infrastructure have increasingly become targets of such attacks. In recent years, ransomware techniques have evolved significantly with the introduction of advanced approaches such as polymorphic malware, double-extortion strategies, and ransomware-as-a-service (RaaS), which make detection and mitigation more challenging.

This study examines ransomware threats from the perspectives of detection mechanisms, preventive security practices, and recovery strategies. Various detection approaches, including signature-based methods, behavioral analysis, and artificial intelligence-driven models, are comparatively analyzed using secondary data from academic publications, cybersecurity reports, and case studies. The analysis indicates that AI-assisted detection systems provide improved adaptability and higher detection accuracy compared with traditional techniques.

The research proposes a layered cybersecurity framework integrating detection, prevention, and recovery mechanisms. Although ransomware cannot be completely eliminated, adopting a structured and proactive security strategy can significantly strengthen organizational resilience and reduce the impact of cyber incidents.