A good direction towards building secure systems that operate efficiently in large-scale environments (like the World Wide Web) is the deployment of Role Based Access Control Methods (RBAC). RBAC architectures do not deal with each user separately, but with discrete roles that users can acquire in the system. The goal of this paper is to present a classification algorithm that during its training phase, classifies roles of the users in clusters. The behavior of each user that enters the system holding a specific role is traced via audit trails and any misbehavior is detected and reported (classification phase). This algorithm will be incorporated in the Role Server architecture, currently under development, enhancing its ability to dynamically adjust the amount of trust of each user and update the corresponding role assignments.