Identification of key predicting factors affecting classified information assurance in institutions of higher learning

The recent escalation in leakages of classified information (CI) has attracted sustained interest from information security scholars and practitioners alike. CI is sensitive information that must be protected from being accessed by unauthorised persons. Thus, the purpose of this research is to ident...

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Bibliographic Details
Main Authors: Ahmadu, Bello, Che Hussin, Ab. Razak, Bahari, Mahadi
Format: Article
Language:English
Published: Human Resource Management Academic Research Society 2022
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Online Access:http://eprints.utm.my/id/eprint/100688/1/BelloAhmadu2022_IdentificationofkeyPredictingFactors.pdf
http://eprints.utm.my/id/eprint/100688/
http://dx.doi.org/10.6007/IJARBSS/v12-i7/10563
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Summary:The recent escalation in leakages of classified information (CI) has attracted sustained interest from information security scholars and practitioners alike. CI is sensitive information that must be protected from being accessed by unauthorised persons. Thus, the purpose of this research is to identify the key factors that influence CI leakages in Institutions of Higher Learning (IHL). In doing this, we conducted a literature survey with a meta-analysis of 19 articles to identify the Key Predicting Factors (KPFs) that influences CI assurance in IHL. The factors found are categorised to organisational (communication structures), regulatory (enforceability), human (social norms, self-efficacy, training, and awareness of being monitored), and technological (internet of data, access control and storage control). These factors were validated via Delphi method to ascertain its consistency by information security experts. This research contributed to the knowledge by identifying KPFs influencing CI violation in IHL. In view of all factors that have been mentioned so far, there is no single information security theory/model that covers all identified KPFs. Therefore, we suggested for the development of a security violation prevention model to safeguard CI in IHL using KPFs.