Vision: “AccessFormer”: Feedback-Driven Access Control Policy Generation Framework

Abstract

Access control failures can cause data breaches, putting entire organizations at risk of financial loss and reputation damage. One of the main reasons for such failures is the mistakes made by system administrators when they manually generate low-level access control policies directly from high-level requirement specifications. Therefore, to help administrators in that policy generation process, previous research proposed graphical policy authoring tools and automated policy generation frameworks. However, in reality, those tools and frameworks are neither usable nor reliable enough to help administrators generate access control policies accurately while avoiding access control failures. Therefore, as a solution, in this paper, we present “AccessFormer”, a novel policy generation framework that improves both the usability and reliability of access control policy generation. Through the proposed framework, on the one hand, we improve the reliability of policy generation by utilizing Language Models (LMs) to generate, verify, and refine access control policies by incorporating the system’s as well as administrator’s feedback. On the other hand, we also improve the usability of the policy generation by proposing a usable policy authoring interface designed to help administrators understand policy generation mistakes and accurately provide feedback.