CCE Theses and Dissertations

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Date of Award


Document Type


Degree Name

Doctor of Philosophy Cybersecurity Management


College of Computing and Engineering


Yair Levy

Committee Member

Wei Li

Committee Member

Ajoy Kumar


Information Technology


Passwords have been used for a long time to grant controlled access to classified spaces, electronics, networks, and more. However, the dramatic increase in user accounts over the past few decades has exposed the realization that technological measures alone cannot ensure a high level of IS security; this leaves the end-users holding a critical role in protecting their organization and personal information. The increased use of IS as a working tool for employees increases the number of accounts and passwords required. Despite being more aware of password entropy, users still often participate in deviant password behaviors, known as ‘password workarounds’ or ‘shadow security.’ These deviant password behaviors can put individuals and organizations at risk, resulting in data privacy. This study, engaging 303 IS users and 27 Subject Matter Experts (SMEs), focused on designing, developing, and empirically validating Password Workaround Cybersecurity Risk Taxonomy (PaWoCyRiT)—a model supported on perceived cybersecurity risks from Password Workarounds (PWWA) techniques and their usage frequency. A panel of SMEs validated the PWWA list from existing literature with recommended adjustments. Additionally, the perception level of the cybersecurity risks of each technique was measured from the 27 SMEs and 303 IS users. They also provided their self-reported and reported on coworkers' engagement frequencies related to the PWWA list. Noteworthy, significant differences were found between SMEs and IS users in their aggregated perceptions of cybersecurity risks of the PWWAs, with IS users perceiving higher risks. Engagement patterns varied between the groups, as well as factors like years of IS experience, gender, and job level had significant differences among groups. The PaWoCyRiT was developed to provide insights into password-related risks and behaviors.