Context-Aware Threat Detection in Multi-Cloud AI Platforms

Abstract

In contemporary enterprise environments, the strategic adoption of multi-cloud AI platforms has unlocked unprecedented capabilities for scalable, resilient, and cost-effective deployment of artificial intelligence (AI) workloads. However, this architectural evolution has simultaneously introduced a complex and expanding attack surface that defies traditional security paradigms. In particular, threat actors exploit the dynamic interplay between heterogeneous cloud services, AI model telemetry, and contextual environmental variables to orchestrate stealthy, high-impact attacks that often elude conventional detection mechanisms. This manuscript presents a comprehensive context-aware threat detection framework expressly designed for multi-cloud AI ecosystems. By ingesting and fusing telemetry from cloud infrastructure logs (e.g., AWS CloudWatch, Azure Monitor, Google Cloud Operations), AI model inference and training logs, and auxiliary context such as workload schedules and user behavior patterns, the proposed system constructs a unified, high-dimensional representation of the operational environment. A hybrid detection engine—comprising supervised gradient boosting machines (GBMs) and unsupervised deep autoencoders—leverages these fused features to identify anomalies and known malicious patterns in real time. Through extensive experimentation within a simulated multi-cloud AI deployment featuring open-source workloads (TensorFlow Serving, Kubeflow pipelines) and adversarial scenarios (stealthy lateral movement, privilege escalation, data exfiltration), the framework achieves a detection accuracy of 92.0%, a false positive rate reduction of 45% relative to context-agnostic baselines, and maintains average alert latency under 1.5 seconds.