Stop guessing at custom cloud roles and start deriving them from data. In dynamic environments like Azure, permission sprawl is a significant risk, where users and services accumulate excessive privileges in overly broad roles, such as "Contributor." This creates a massive, unnecessary attack surface that manual audits can't keep pace with. This session introduces a practical, PowerShell-driven pipeline that transforms this guesswork into a repeatable, data-driven security practice.

We will demonstrate how to turn raw cloud activity logs into precise, least-privilege RBAC roles, all using code that works on both PowerShell 7 and Windows PowerShell 5.1. We will walk through the entire workflow: ingesting and shaping data into a user-action matrix, applying K-Means clustering to discover natural usage patterns, and using our custom "auto-k" algorithm to determine the optimal number of roles intelligently. This technique prevents both unmanageable "role explosion" and overly permissive mega-roles, producing a ready-to-deploy JSON role definition that reflects how your users *actually* work.

To accelerate the final steps, we also showcase a strictly optional AI assistant that suggests business-friendly role names and descriptions—all while keeping a human firmly in the loop. You will leave with a blueprint to shrink your organization's attack surface and all the code needed to adapt this methodology for Azure, AWS, and Google Cloud.