The rapid migration of enterprise workloads to hyperscale cloud environments has fundamentally transformed the global IT landscape, introducing unprecedented scalability alongside a radically expanded attack surface. Traditional security frameworks, reliant on static rules and siloed detection engines, are increasingly incapable of managing the high-velocity, polymorphic threats characteristic of modern cloud-native infrastructures. This review explores the paradigm shift toward Hybrid AI Models for Cloud Security Optimization. Hybrid AI—defined here as the synergistic integration of diverse machine learning (ML) paradigms, such as combining supervised learning for known threat classification with unsupervised learning for zero-day anomaly detection—provides a multi-layered defensive posture. By leveraging the automated feature extraction of Deep Learning (DL) alongside the structural interpretability of classical algorithms like Random Forests or Support Vector Machines, hybrid models achieve superior precision in identifying stealthy \\\"living-off-the-land\\\" (LotL) attacks and lateral movement. This article categorizes current hybrid methodologies, including the fusion of Graph Neural Networks (GNNs) for mapping relational cloud topologies and Reinforcement Learning (RL) for autonomous incident response. We examine how these models optimize security operations by reducing false-positive rates and automating the \\\"OODA loop\\\" (Observe, Orient, Decide, Act) at machine speed. Furthermore, the review addresses the critical challenges of data drift in elastic environments, the \\\"black-box\\\" transparency problem, and the necessity for Federated Learning to ensure privacy in multi-tenant architectures. By synthesizing recent academic breakthroughs and industrial case studies, this paper provides a strategic roadmap for building resilient, self-healing cloud ecosystems.
Rahul Kapoor (Sun,) studied this question.