AI-Based Vulnerability Prediction In Cloud Infrastructure

As cloud computing becomes the backbone of modern digital enterprises, the complexity of its infrastructure—comprising virtualized resources, containerized microservices, and serverless architectures—has expanded the attack surface exponentially. Traditional reactive security measures, which rely on signature-based detection and manual patching, are increasingly inadequate against zero-day exploits and sophisticated persistent threats. This review explores the paradigm shift toward AI-based proactive vulnerability prediction. By leveraging Machine Learning (ML) and Deep Learning (DL) algorithms, security frameworks can now analyze massive streams of telemetry data, network logs, and system calls to identify latent weaknesses before they are exploited. This article categorizes current AI methodologies, including supervised learning for known patterns and unsupervised anomaly detection for novel threats. We examine the integration of these models within DevSecOps pipelines and the specific challenges posed by multi-tenant cloud environments. Furthermore, the review addresses the \\\"black box\\\" nature of AI models, emphasizing the growing need for Explainable AI (XAI) in security operations to provide actionable insights for human operators. By synthesizing recent research and industry applications, this paper provides a roadmap for future developments in automated threat modeling and self-healing cloud systems. The findings suggest that while AI significantly reduces the mean time to detect (MTTD), its efficacy is intrinsically tied to data quality and the adversarial robustness of the models themselves.