How the Adoption of the Big-Data Paradigm Affects the Key Factors That Influence the Effectiveness of an Information Assurance (IA) Framework: A Multiple-Case Study

This qualitative study identified those factors that influence the perceived effectiveness of traditional IA control frameworks. The key factors examined in this study are risk management, governance, access control, privacy protection, integrity, availability, reliability, and usability. The researcher endeavored to determine how the effectiveness of the factors of effectiveness is impacted when the IA frameworks are applied to a big-data environment within the context of the unified theory of acceptance and use of technology (UTAUT) Model. The multiple-case study approached the issue from the perspective of three operational groups, senior decision makers, information assurance professionals, and information security practitioners across three organizations. Data was gathered by face-to-face interview, direct observation, and historic documentation review. Gathered data was processed and evaluated by use of the NVivo 10 software process. The data gathered and analyzed during the multiple-case study leads one to infer that traditional IA control frameworks are engineered to take advantage of the foundational controls of a traditional network-centric data base environment. In a traditional data base environment, the data base management software provides controls such as read/write, content type, and audit logging which are the foundation for the keys of effectiveness. In a big data environment, those foundational controls must be provided by intention through policy, structure, or performance agreement, as opposed to by implementation. Thus, while the key factors of traditional controls are perceived as structurally sound and effective, to remain effective in a big data environment traditional controls and their associated key factors require some level of re-engineering. Or, as in the case of training, greater application is required to gain the perception of trust in a big data environment. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]