Authors: Kavita L. Desai
Abstract: The rapid adoption of hybrid cloud architectures has transformed modern enterprise computing by offering scalability, flexibility, and cost efficiency. However, this transformation has also introduced complex security challenges stemming from heterogeneous infrastructures, dynamic workloads, and distributed data environments. Traditional rule-based and signature-driven security mechanisms have proven inadequate in addressing sophisticated cyber threats such as zero-day attacks, insider breaches, and advanced persistent threats (APTs). In response, Artificial Intelligence (AI)-based anomaly detection has emerged as a crucial innovation in hybrid cloud security. By leveraging machine learning algorithms, AI systems can identify deviations from normal behavioral patterns in real time, enabling early detection and mitigation of potential intrusions. This review paper explores the impact of AI-based anomaly detection on securing hybrid cloud networks. It examines the foundational aspects of hybrid cloud security, outlines the principles and mechanisms of AI-driven anomaly detection, and discusses practical applications in network monitoring, threat intelligence, and automated response. The paper also analyzes key challenges, including data imbalance, model interpretability, and privacy constraints, while comparing AI-based solutions with traditional detection systems. Furthermore, future research directions are highlighted, focusing on explainable AI, federated learning, quantum-driven analytics, and autonomous defense frameworks. The findings underscore that AI-based anomaly detection is not only enhancing real-time visibility and threat response but also paving the way toward predictive, self-healing, and intelligent hybrid cloud security ecosystems.
DOI: http://doi.org/10.5281/zenodo.17799638
