AI-Augmented Cloud Security Posture Management for Securing Enterprise AI Workloads

Cloud Security Posture Management (CSPM) has historically targeted infrastructure misconfigurations - exposed storage buckets, overprivileged IAM roles, and open network ports. The proliferation of AI workloads across enterprise clouds introduces fundamentally novel attack vectors: GPU resource hijacking, model training data exfiltration, shadow AI deployments, prompt injection surfaces, and insecure ML pipeline configurations. Existing CSPM frameworks lack semantics for AI asset discovery, AI-specific policy enforcement, and behavioral anomaly detection across ephemeral GPU compute clusters. This paper proposes AI-Aware CSPM (AA-CSPM), an architectural extension that integrates ML-based risk scoring, AI-specific misconfiguration detection rules, data lineage monitoring, and convergent posture management across CSPM, Data Security Posture Management (DSPM), and Cloud Infrastructure Entitlement Management (CIEM). We formalize a threat taxonomy of twelve AI-era attack classes, implement detection pipelines across three major cloud providers (AWS SageMaker, Google Vertex AI, Azure ML), and benchmark AA-CSPM against three baseline CSPM platforms. AA-CSPM achieves a mean detection accuracy of 94.3%, a false-positive reduction of 38.7%, and reduces mean time-to-detect (MTTD) for AI-specific misconfigurations by 61.4% over legacy baselines. These results demonstrate that AI workloads constitute a distinct and underserved risk domain that demands dedicated posture management tooling.