Design and Implementation of AI-Powered Intrusion Detection Systems in Enterprise Networks: A Comprehensive Review

Abstract

Enterprise networks face increasingly sophisticated and diverse cyber threats that challenge traditional intrusion detection approaches. Artificial Intelligence has emerged as a transformative technology for enhancing intrusion detection capabilities, offering improved accuracy, adaptability, and automation in identifying malicious activities across complex network infrastructures. This comprehensive review examines the design principles, implementation strategies, and operational considerations for AI-powered Intrusion Detection Systems (IDS) in enterprise environments. We systematically analyze various AI techniques including machine learning, deep learning, and ensemble methods applied to network intrusion detection, evaluating their effectiveness across different attack categories and network architectures. The paper explores critical implementation challenges including high-dimensional data processing, real-time detection requirements, class imbalance, concept drift, and adversarial evasion techniques. We examine prominent benchmark datasets, evaluation methodologies, and performance metrics used in IDS research. Furthermore, we discuss architectural considerations for deploying AI-powered IDS in heterogeneous enterprise networks, including distributed detection frameworks, cloud-native implementations, and integration with existing security infrastructure. Finally, we identify emerging trends and future research directions, including explainable AI for security operations, federated learning for collaborative threat intelligence, and adaptive systems capable of autonomous evolution against emerging threats. This review provides researchers and practitioners with comprehensive insights into the current state and future trajectory of AI-powered intrusion detection technologies.