On the Comparative Analysis of Trends in Cybersecurity Risk Assessment, Governance, and Compliance Frameworks

This paper proposes an evaluation framework and systematic review of recent trends for cybersecurity risk assessment governance and compliance. The proposed framework incorporates several metrics for assessing model effectiveness. The findings highlight research opportunities in scalable privacy-preservation techniques, cross-domain validation, and standardized performance benchmarks. Federated learning models achieve the highest privacy rating while maintaining strong performance in precision and automation, suggesting distributed learning architectures as a promising direction for future governance, risk, and compliance framework development. Based on these findings, we recommend for future frameworks supported by regulatory considerations that balance privacy, performance, and ethical requirements, and combine quantum-resistant architectures with privacypreserving features.