Companies have unprecedented access to candidate data, which, despite the obvious benefits, brings significant concerns about privacy. One of the large gaps in the literature is the issue of "users underestimating cybersecurity threats" (Bauer et al., 2020). Despite growing awareness around data privacy and security, many users, including candidates and companies, continue to overlook or underestimate the risks posed by cyber threats during the recruitment process. This study represents the development and validation of a Cybersecurity Knowledge (CSK) Scale, designed to assess job applicants' CSK in selection processes. In comparison to similar existing scales, this CSK scale will be specifically developed for and valid in an online hiring context. The process follows the gold-standard practices of scale development (Hinkin, 1998; Zickar, 2020), through four stages: initial item development with the input of a computer science expert, exploring the underlying structure of CSK and further item refinement, finalising and testing the scale factor structure). Content validity is tested though an iterative item generation and review process. Furthermore, the nomological network of CSK is examined to analyse evidence for convergent and discriminant validity. Theoretical and practical implications for the CSK measure are discussed.