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Abstract
With the rise in sophisticated cyber threats, traditional security measures have proven insufficient in addressing real-time security risks. Dynamic risk assessment (DRA) models leverage predictive threat intelligence to proactively mitigate cybersecurity incidents. This paper explores the evolution of DRA models, integrating machine learning, artificial intelligence (AI), and behavioral analytics to enhance threat detection and incident response. A systematic literature review of prior research highlights key advancements, challenges, and the future direction of predictive cybersecurity risk management. Findings indicate that dynamic models outperform static assessment methods by improving adaptability and accuracy in complex environments. The paper also presents comparative analyses of various models and their effectiveness in proactive risk mitigation.
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