Access control (AC) systems play an important role in ensuring security by regulating how resources are accessed, protecting sensitive information, and maintaining system integrity. Their complexity arises not only from diverse policies and mechanisms but also from the involvement of multiple stakeholders, including resource owners, administrators, and end-users. Taking inspiration from explainable AI and explainable security, we define the first model of access control explainability, as a quality measure of the explanation graph constructed around the decisions made within the AC system. We then explore the literature to identify how existing work can be integrated as explanatory processes. Finally, we leverage our framework to articulate three open research challenges: the collection and interpretation of AC decisions, the effective construction of AC explanation graphs, and the definition of meaningful and computationally efficient explanation quality metrics.

Policy comprehension is crucial for ensuring data protection. Yet, policies written in flexible and expressive languages such as XACML are not easy to comprehend. In this work, we propose a visualization framework to facilitate the comprehension of XACML policies and their evaluation. Our framework shows a tree representation of the XACML policies to be enforced and highlights the contribution of its policy elements to the overall access decision, thus supporting the understanding of how this decision resulted from the interplay between possibly conflicting access requirements. We implemented our visualization framework as an extension to SAFAX, an XACML-based framework that offers authorization as a service.

International audience

Access control is a fundamental component of IT systems to guarantee the confidentiality and integrity of sensitive resources. However, access control systems have inherent limitations: once permissions have been assigned to users, access control systems do not provide any means to prevent users from misusing such permissions. The problem of privilege misuse is typically addressed by employing auditing mechanisms, which verify users’ activities a posteriori. However, auditing does not allow for the timely detection and mitigation of privilege misuse. In this work, we propose a framework that complements access control with anomaly detection for the run-time monitoring of access requests and raises an alert when a user diverges from her normal access behavior. To detect anomalous access requests, we propose a novel approach to build user profiles by eliciting patterns of typical access behavior from historical access data. We evaluated our framework using the access log of a hospital. The results show that our framework has very few false positives and can detect several attack scenarios.