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This article details the development of a behavioral clustering model designed to map the functional roles of cloud identities by analyzing activity patterns within audit logs. By utilizing unsupervised machine learning algorithms like UMAP and HDBSCAN, researchers analyzed over 40,000 identities across 125 cloud environments to differentiate between true behaviors and assigned IAM policies. This approach allows security teams to identify distinct roles such as administrators, DevOps, and security tools, providing a more accurate baseline for threat detection than static permissions alone.
Beyond identification, the study demonstrates how to distill complex clustering results into lightweight heuristic logic using logistic regression with L1 regularization. This simplified logic can be implemented directly via standard SQL queries, enabling organizations to perform high-scale identity classification and anomaly detection without maintaining resource-intensive machine learning pipelines. The methodology provides a robust blueprint for enhancing cloud detection and response (CDR) across various platforms including AWS, Kubernetes, and SaaS environments.
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