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ABSTRACT To avoid insider computer misuse, identity, and authorization data referring to the legitimate users of systems must be properly organized, constantly and systematically analyzed, and evaluated. In order to support this, structured and secure Identity Management is required. A comprehensive methodology supporting Identity Management within organizations has been developed, including gathering of identity data spread among different applications, systematic cleansing of user account data in order to detect semantic as well as syntactic errors, grouping of privileges and access rights, and semiautomatic engineering of user roles. The focus of this paper is on the cleansing of identity and account data leading to feedback where insider misuse due to existing privileges which go beyond the scope of the users' current need-to-know may occur. The paper in detail presents used data cleansing mechanisms and underlines their applicability in two real-world case studies. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

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]