ENHANCING NETWORK SECURITY WITH AI AND PROACTIVE MEASURES – A REVIEW

One of the most important tasks is to find smart solutions that keep pace with the rapid development of cyber threats, due to the inability of traditional network security mechanisms to keep pace with them. This paper proposes AI-based ideas to address these threats and their evolution, leading us to a proactive, self-learning architecture using advanced machine learning algorithms. This architecture is designed to detect anomalies and predict cyber attacks, providing an autonomous response to all threats in a timely manner. The open model takes into account behavioral analytics and threat intelligence, along with modern learning classifications, to achieve detection accuracy and reduce false positives. The results show that the approach using AI significantly improves the efficiency of threat analysis, reduces response time, and gives the system more resilience against advanced attacks. This research provides us with a scalable methodology for modern systems that support cloud computing and decentralized systems.