Abstract

In an era of increasing digitalization, cyber risks pose significant challenges to project resilience and organizational success. This study explores the integration of predictive analytics into cyber risk management frameworks, emphasizing its role in proactively identifying, assessing, and mitigating potential threats. Leveraging a combination of statistical techniques, machine learning models, and real-time monitoring, the research highlights the effectiveness of data-driven strategies in reducing vulnerabilities and enhancing decision-making. Key findings demonstrate the predictive power of factors such as incident impact scores and threat frequency, as well as the superior performance of neural networks in forecasting risks. The study also underscores the importance of anomaly detection in real-time cyber defense. While challenges such as data quality and system integration remain, the results affirm the transformative potential of predictive analytics in securing project outcomes and fostering organizational resilience. Future research should focus on refining predictive models and addressing scalability issues to further strengthen cyber risk management practices