Practical risk aggregation in RBAC models

Abstract

This paper describes our system, built as part of a commercially available product, for inferring the risk in an RBAC policy model, i.e., the assignment of permissions to roles and roles to users. Our system implements a general model of risk based on any arbitrary set of properties of permissions and users. Our experience shows that fuzzy inferencing systems are best suited to capture how humans assign risk to such assignments. To implement fuzzy inferencing practically we need the axiom of monotonicity, i.e., risk can not decrease when more permissions are assigned to a role or when the role is assigned to fewer users. We describe the visualization component which administrators can use to infer aggregate risk in role assignments as well as drill down into which assignments are actually risky. Administrators can then use this knowledge to refactor roles and assignments. Copyright 2012 ACM.