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Conversational user interfaces (CUIs), such as chatbots, are becoming a common component of many software systems. Although they are evolving in many directions (such as advanced language processing features, thanks to new AI-based developments), less attention has been paid to access control and other security concerns associated with CUIs, which may pose a clear risk to the systems they interface with. In this paper, we apply model-driven techniques to model and enforce access-control policies in CUIs. In particular, we present a fully fledged framework to integrate the role-based access-control (RBAC) protocol into CUIs by: (1) modeling a set of access-control rules to specify permissions over the bot resources using a domain-specific language that tailors core RBAC concepts to the CUI domain; and (2) describing a mechanism to show the feasibility of automatically generating the infrastructure to evaluate and enforce the modeled access control policies at runtime. [ABSTRACT FROM AUTHOR]

Patient consent is currently a missing piece on Electronic Health Records System (EHR-S) access permission. The control is needed to ensure personal data as the property of the individual, not data controllers or health-care service providers. To cope with this need, in this article, an adaptation of existent Role-Based Access Control (RBAC), including patient-centric control, is described. The revisited feature of existing administrative and supporting RBAC functions allows exclusive control orchestrated by the patient as sole information owner, including the ability to encrypt their data for confidentiality purposes. The additions mimic a Discretionary Access Control (DAC) capability using existing user group membership to vet access over symmetric keys bind to patient's data via the associated PERMS matrix. [ABSTRACT FROM AUTHOR]