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The rapid evolution of cyber threats has significantly increased the complexity of risk
management in modern digital infrastructures. Traditional Risk and Control SelfAssessment (RCSA) approaches largely rely on periodic manual assessments and static
control evaluation, which are insufficient for addressing real-time cyber risks in dynamic
environments. This research proposes an advanced machine learning–driven framework
for enhancing RCSA processes through continuous monitoring, intelligent threat
detection, and automated risk evaluation
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