As modern networks become more distributed and high-throughput, traditional intrusion detection systems struggle to keep pace with evolving attack patterns. Signature-based approaches, while effective for known threats, fail to detect zero-day exploits and polymorphic attacks. The integration of artificial intelligence into intrusion detection systems introduces adaptive, data-driven capabilities that enable real-time threat detection, anomaly identification, and automated response across complex environments. At the core of AI-powered IDS lies the use of Machine Learning and Deep Learning techniques to model normal and malicious behavior. Supervised learning algorithms, such as random forests and support vector machines, are trained on labeled datasets to classify network traffic. However, in real-world scenarios where labeled data is scarce, unsupervised and semi-supervised methods, including clustering and autoencoders, are often preferred for anomaly detection.…