AI-Driven Cloud Security: AIOps for Threat Detection and Compliance

Abstract

The rapid growth of cloud and hybrid computing has brought significant scale, complexity, and security challenges to IT operations. Traditional rule-based monitoring systems and signature-based Security Information and Event Management (SIEM) tools are no longer sufficient to process the enormous volume of events generated in modern environments or to provide timely, accurate detection of incidents. Artificial Intelligence for IT Operations (AIOps) has emerged as a transformative approach by combining machine learning, predictive modeling, big data analytics, and automation to improve anomaly detection, optimize resource allocation, and accelerate the process of identifying root causes. Empirical studies report that AIOps platforms can reduce mean time to detection by nearly half and cut audit preparation time by up to 60%, underscoring their advantages over conventional methods. In addition to performance monitoring, AIOps is increasingly applied to security and compliance, enabling automated evidence collection, support for zero-trust architectures, and AI-assisted remediation workflows. Despite these benefits, reliance on opaque “black-box” models raises concerns around explainability, accountability, and regulatory compliance, particularly in mission-critical domains. Multi-cloud and hybrid infrastructures further complicate deployment due to interoperability issues, data silos, and risks of algorithmic bias. This paper reviews academic and industry work on AI-driven cloud security and operations from 2022 to 2025, outlines a taxonomy of AIOps functions spanning detection, compliance, response, and governance, and identifies unresolved challenges such as adversarial resilience, transparency, and multi-cloud coordination. Finally, future directions are discussed, including explainable and neuro-symbolic AIOps, federated analytics for distributed environments, and autonomous self-healing infrastructures. The review aims to provide researchers and practitioners with a consolidated reference for developing trustworthy, scalable, and secure AI-driven cloud operations.