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Identity and access management (IAM) has become one main challenge for companies over the last decade. Most of the medium-sized and large organizations operate standardized IAM infrastructures in order to comply with regulations and improve the level of IAM automation. A recent trend is the application of attribute-based access control (ABAC) for automatically assigning permissions to employees. The success of ABAC, however, heavily relies on the availability of high-quality attribute definitions and values. Up to now, no structured attribute quality management approach for IAM environments exists. Within this paper, we propose TAQM, a comprehensive approach building on a tool-supported structured process for measuring and improvement of IAM data quality. During the evaluation of three real-life use cases within large industrial companies we underline the applicability of TAQM for the identification and cleansing of attribute errors by IT and non-IT experts as well as the general introduction of quality management processes for IAM. [ABSTRACT FROM AUTHOR]

Organizations undertake complex and costly projects to model high-quality Access Control Policies (ACPs). Once built, these policies must be maintained and managed in an ongoing process to keep their quality high. Insufficient maintenance leads to inaccurate authorization decisions and increases the policies' administrative effort and susceptibility to errors. While the initial modeling of ACPs has received significant research interest, their optimization is not yet covered as broadly. This work provides a theoretical foundation for ACP quality and its optimization. Furthermore, it analyzes how existing research addresses optimization of ACPs with regard to six crucial optimization dimensions. It presents a structured literature survey tracing these optimization dimensions, the contributed research artifact and data requirements. Building on this literature catalogue, this work elaborates on inaccuracies for user permission assignments, data availability, minimal perturbation and recommendation-based optimization. [Display omitted] • Derivation of ACP optimization based on data quality and 16 well-established criteria. • Literature survey on ACP optimization based on six optimization objectives. • Discussion and findings for ACP optimization [ABSTRACT FROM AUTHOR]