AI-Powered Cybersecurity: How Machine Learning is Redefining Threat Detection and Prevention

The rate at which the cyber threats keep on evolving exceeded that of the conventional security mechanisms and hence, requires a shift from traditional security mechanisms to intelligent, adaptive and scalable ways of defending the networks. From real time threat detection to automating responses, to predictive risk assessment, Artificial Intelligence (AI), especially Machine Learning (ML) is shaping cybersecurity. In this paper, we discuss how machine learning models i.e. supervised and unsupervised learning, deep learning support in achieving intrusion detection, malware classification, and anomaly detection. For instance, the current paper analyzes key methodologies like Support Vector Machines (SVMs), Random Forests (RF) and Neural Networks as they prove applicable in.detecting of a sophisticated cyberattack. We additionally take up tasks like adversarial attack, model interpretability, and data privacy. Using empirical analysis, we present comparative performance metrics of MLdriven cybersecurity solutions highlighting better performance of MLdriven solutions compared to traditional rule-based systems. Based on findings, AI powered cybersecurity does not only reinvent the way on threats prevention but it opens doors for autonomous and self learning security framework. Finally, the paper discusses the future directions, especially related to explainable AI, federated learning and hybrid AI based security models, so that the robust and trusted cyber defense mechanisms can be assured. Keywords: Artificial Intelligence, Cybersecurity, Machine Learning, Threat Detection, Anomaly Detection, Intrusion Detection Systems, Deep Learning, Adversarial Attacks, AI-driven Security, Automated Threat Prevention