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This research introduces a behavioral clustering model developed to map cloud identities to functional roles by analyzing activity patterns in cloud audit logs. By examining over 40,000 identities across 125 environments, the study demonstrates how unsupervised machine learning—specifically UMAP and HDBSCAN—can categorize accounts such as administrators, DevOps, and backup services based on actual behavior rather than static IAM permissions. This approach addresses the security challenge of masquerading techniques and over-privileged accounts that often evade traditional detection.
The methodology utilizes a multi-stage pipeline involving data ingestion, vectorization, and dimensionality reduction. Beyond initial identification, the researchers showcase how complex behavioral patterns can be distilled into lightweight, interpretable logistic regression models. These models allow organizations to implement automated role classification at scale using standard SQL, providing continuous visibility and enhancing threat detection by flagging high-risk anomalies that deviate from established functional baselines.
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