In the modern world, where cyber threats are increasingly complex and frequent, traditional security methods often prove inadequate. Machine Learning (ML) offers a more proactive solution through the concept of predictive cybersecurity, which aims to prevent threats before they occur. This paper examines the role of ML in transforming threat management, emphasizing its ability to analyze large data volumes and identify early warning signs of security breaches. ML techniques, such as anomaly detection, malware classification, and behavior analysis, enhance the ability to detect and prevent threats in real-time. Additionally, the paper addresses the challenges of ML in cybersecurity, such as the need for large datasets, algorithmic biases, and the constantly evolving threat landscape. The potential for combining ML with other technologies, including artificial intelligence and big data, is also explored, highlighting how these integrations can strengthen cybersecurity defenses. Finally, the paper discusses the future of predictive cybersecurity, focusing on innovations like neural networks and autonomous systems that may revolutionize threat detection and response. By synthesizing insights from current literature and case studies, this work provides practical guidance for cybersecurity professionals in adopting ML solutions to mitigate evolving cyber risks.

Machine Learning Predictive Cybersecurity Threat Management Anomaly Detection Malware Classification Artificial Intelligence Neural Networks