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Article
Publication date: 14 August 2018

Tejaswini Herath, Myung-Seong Yim, John D’Arcy, Kichan Nam and H.R. Rao

Employee security behaviors are the cornerstone for achieving holistic organizational information security. Recent studies in the information systems (IS) security literature have…

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Abstract

Purpose

Employee security behaviors are the cornerstone for achieving holistic organizational information security. Recent studies in the information systems (IS) security literature have used neutralization and moral disengagement (MD) perspectives to examine employee rationalizations of noncompliant security behaviors. Extending this prior work, the purpose of this paper is to identify mechanisms of security education, training, and awareness (SETA) programs and deterrence as well as employees’ organizational commitment in influencing MD of security policy violations and develop a theoretical model to test the proposed relationships.

Design/methodology/approach

The authors validate and test the model using the data collected from six large multinational organizations in Korea using survey-based methodology. The model was empirically analyzed by structural equation modeling.

Findings

The results suggest that security policy awareness (PA) plays a central role in reducing MD of security policy violations and that the certainty of punishment and immediacy of enforcing penalties are instrumental toward reducing such MD; however, the higher severity of penalties does not have an influence. The findings also suggest that SETA programs are an important mechanism in creating security PA.

Originality/value

The paper expands the literature in IS security that has examined the role of moral evaluations. Drawing upon MD theory and social cognitive theory, the paper points to the central role of SETA and security PA in reducing MD of security policy violations, and ultimately the likelihood of this behavior. The paper not only contributes to theory but also provides important insights for practice.

Article
Publication date: 1 March 2024

Mohan Thite and Ramanathan Iyer

Despite ongoing reports of insider-driven leakage of confidential data, both academic scholars and practitioners tend to focus on external threats and favour information…

Abstract

Purpose

Despite ongoing reports of insider-driven leakage of confidential data, both academic scholars and practitioners tend to focus on external threats and favour information technology (IT)-centric solutions to secure and strengthen their information security ecosystem. Unfortunately, they pay little attention to human resource management (HRM) solutions. This paper aims to address this gap and proposes an actionable human resource (HR)-centric and artificial intelligence (AI)-driven framework.

Design/methodology/approach

The paper highlights the dangers posed by insider threats and presents key findings from a Leximancer-based analysis of a rapid literature review on the role, nature and contribution of HRM for information security, especially in addressing insider threats. The study also discusses the limitations of these solutions and proposes an HR-in-the-loop model, driven by AI and machine learning to mitigate these limitations.

Findings

The paper argues that AI promises to offer many HRM-centric opportunities to fortify the information security architecture if used strategically and intelligently. The HR-in-the-loop model can ensure that the human factors are considered when designing information security solutions. By combining AI and machine learning with human expertise, this model can provide an effective and comprehensive approach to addressing insider threats.

Originality/value

The paper fills the research gap on the critical role of HR in securing and strengthening information security. It makes further contribution in identifying the limitations of HRM solutions in info security and how AI and machine learning can be leveraged to address these limitations to some extent.

Details

Personnel Review, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0048-3486

Keywords

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