Abstract:

The accelerated development of digital technologies has created an exponential growth of cyber threats and vulnerabilities, which present serious risks to individuals, organizations, and national infrastructures. The conventional cybersecurity systems, which only depend on signature-based detection and human intervention, are increasingly found wanting in coping with the complexity and magnitude of contemporary cyberattacks. Such systems tend to lack the capability of detecting new or sophisticated persistent threats, causing delay in responding and more risk for damage. With these, AI has developed as a game-changer in a position to change the game for cybersecurity management [12]. Through machine learning and deep learning methods, AI can increase accuracy in detecting threats, shorten response times, and facilitate predictive analytics for anticipation in defense. In addition, AI-based systems can evolve with changing threat environments, enhance incident response automation, and strengthen overall system resilience. This research explores the use of AI in cybersecurity, assessing different models and architectures for intrusion detection, anomaly detection, and real-time response [13]. The research illustrates how AI-based frameworks excel over conventional systems in speed, scalability, and detection effectiveness.