Article Review #1

Cybersecurity Awareness and Reporting Behavior: The Role of AI and Training

Introduction

Muthuswamy and Esakki (2024) investigate how cybersecurity knowledge, AI-related concerns, and training influence employees’ likelihood to report suspicious activity. According to the study, cybersecurity training increases employees’ knowledge of AI hazards but has no meaningful effect on their intentions to utilize AI in security operations. The study emphasizes the impact of organizational policies in driving security behavior, as well as the value of cybersecurity education in improving incident response and reducing employee stress.

Social Science Principles in Cybersecurity Awareness

The article connects with several foundational principles of social science, including objectivity, parsimony, and empiricism. The principle of objectivity emphasizes the need for unbiased research, where conclusions are not swayed by personal opinions. This is particularly important when measuring cybersecurity behaviors, as it ensures that the findings are based on empirical data rather than preconceived notions (Ngamcharoen et al., 2024). In line with this, the principle of parsimony is demonstrated by the researchers’ effort to simplify the complex human behaviors associated with cybersecurity. Rather than attributing these behaviors to a myriad of factors, the study focuses on a limited set of measurable variables, such as students’ awareness of cyber threats and their password management practices (Ngamcharoen et al., 2024). Finally, the principle of empiricism is central to the research methodology, which relies on real-world data gathered through surveys and statistical analysis to draw conclusions about students’ cybersecurity behaviors. By grounding their findings in empirical research, the authors ensure the validity and reliability of their tools for measuring cybersecurity practices among students (Ngamcharoen et al., 2024).

Key Hypotheses and Findings

The study aims to better understand how cybersecurity knowledge, perceived AI dangers, and training affect incident reporting. The major hypotheses are:

H7: Cybersecurity training improves the link between AI usage intent and reporting questionable conduct (rejected).

H8: Cybersecurity training improves the relationship between perceived AI risks and the reporting of behavior (accepted).

The findings indicate that, while training helps employees spot dangers, variables such as workplace culture and views of AI efficacy play a larger impact in deciding whether employees report suspicious activity.

Methodology

The research collects self-reported data from individuals across businesses using a cross-sectional survey. Regression analysis is a statistical tool used to investigate the links between cybersecurity awareness, AI issues, training, and reporting behavior. The study admits that certain biases, such as societal desire and recollection bias, could hinder data accuracy. While the study’s approach is useful for discovering connections, it does not demonstrate connection.

Cybersecurity Training and AI-Related Threats

The researchers apply statistical modeling to investigate how cybersecurity training influences employees’ responses to AI-related security concerns. The statistics show that cybersecurity training increases AI threat detection but does not raise the chance of reporting suspicious activity. The study reveals that business regulations, perceived AI performance, and employee trust in the safety of AI may have a greater impact on reporting activity than training itself.

Conceptual Connections and Influence

The article is closely tied to concepts in this course, especially those surrounding the human-factors approach to understanding cybersecurity. As we explore in the course, cybersecurity is not only a technical challenge but also a social issue. The study highlights how social influences, like peer behavior and institutional norms, shape students’ cybersecurity practices. This aligns with the class focus on understanding how social factors contribute to cybersecurity risks, a key aspect of our multidisciplinary approach. Additionally, the research methods discussed in the article mirror the social science framework we use in class, emphasizing empirical data collection to analyze human behaviors and decision-making processes. The tools developed in the study can be seen as a practical application of the psychological and sociological theories we learn about, such as those addressing individual behaviors and societal norms surrounding cybersecurity. By focusing on the intersection of human behavior and cybersecurity, the article ties into our course’s goal of applying social science theories to cybersecurity, helping us understand and address the real-world behaviors that lead to cyber risks (Ngamcharoen et al., 2024).

Challenges for Marginalized Groups

The study focuses on issues for underrepresented populations, including challenges to training in cybersecurity and biases in reporting incidents. Employees from underrepresented groups may experience structural barriers to training or be concerned about retaliation if they report threats. Creating a fair security culture requires inclusive training in cybersecurity and neutral reporting procedures.

Practical Implications and Future Research

This study examines how cybersecurity training improves workplace security and decreases stress by increasing confidence in employees in dealing with AI-related dangers. The study offers insights for businesses looking to create organized systems for reporting incidents and training programs directed to AI security issues. It also emphasizes ethical aspects in AI adoption to enable responsible deployment and workforce preparation.

Conclusion

The study found that, while cybersecurity training increases awareness, it has no direct influence on behavior being reported. Administrative regulations and perceived AI dangers have a greater impact on incident reporting. Future study should look into leadership impact, industry-specific cybersecurity concerns, and how to integrate AI securely while reducing workplace stress.

References

Muthuswamy, Vimala , and Suresh Esakki. “Impact of Cybersecurity and AI’s Related Factors OnIncident Reporting Suspicious Behaviour and Employees Stress: Moderating Role of Cybersecurity Training.” Cyber Crime Journal, Jan. 2024, cybercrimejournal.com/menuscript/index.php/cybercrimejournal/article/view/330/99.

Article Review #2

Evaluating Cybersecurity Behavior Measurement Tools for College Students

Introduction

The article “Development and Evaluation on Cybersecurity Behaviour Measurement Instruments for Undergraduate Students” by Pannika Ngamcharoen, Naksit Sakdapat, and Duchduen Emma Bhanthumnavin focuses on creating tools to measure the cybersecurity behaviors of undergraduate students. The study emphasizes the importance of reliable measurement tools in improving cybersecurity practices and offers insights on how to target interventions for better cybersecurity education. The article evaluates measurement tools designed to assess cybersecurity behaviors among college students. The authors explore how these tools can guide interventions to improve digital safety in college settings by identifying key factors that influence student behavior.

Social Science Principles

Several social science principles are evident in the article. The first is social influence, seen in how peers, faculty, and the broader social environment shape students’ cybersecurity habits. The authors (Ngamcharoen et al., 2024) note that these tools help identify the social factors affecting cybersecurity choices. Another principle, social norms, is explored by examining how students’ behaviors align with or deviate from accepted cybersecurity practices. Finally, the study discusses behavioral change theory, looking at how these tools can promote safer online behaviors (Ngamcharoen et al., 2024).

Research Question and Hypotheses

The research question asks how well the newly developed instruments can measure students’ cybersecurity behaviors. The authors hypothesize that students who score higher on these tools will demonstrate safer cybersecurity practices, such as stronger passwords and more caution regarding phishing (Ngamcharoen et al., 2024).

Research Methods and Data Analysis

The study uses a quantitative methodology, specifically developing and testing tools to measure cybersecurity behavior. Surveys, including Likert-scale questions and situational judgment tests, were used to collect data. The analysis, including factor analysis, demonstrated that the tools could measure behaviors like password management and awareness of cyber threats (Ngamcharoen et al., 2024). The research also found that demographic variables like major or year of study influenced students’ cybersecurity practices.

Class Concepts and Relevance

This article directly relates to concepts discussed in the course, especially how human factors influence cybersecurity behavior. Ngamcharoen et al. (2024) apply psychological and social theories to assess and modify students’ cybersecurity behaviors. The study explores how social influences, norms, and behavior change theories can be used to improve students’ cybersecurity practices, directly tying into the course’s multidisciplinary approach to cybersecurity.

Marginalized Groups and Societal Contributions

The study is particularly relevant for marginalized groups, especially students from disadvantaged backgrounds who may lack formal cybersecurity education. Ngamcharoen et al. (2024) argue that interventions based on these tools can help address these gaps. The study contributes to cybersecurity education by offering evidence-based approaches to improve behavior and identify knowledge gaps among students.

Conclusion

The article stresses the importance of developing accurate measurement tools to assess and improve cybersecurity behaviors. Ngamcharoen et al. (2024) demonstrate how such tools can enhance cybersecurity education, particularly for students in vulnerable environments. Their research provides valuable insights into creating effective cybersecurity education programs and interventions.

References

Ngamcharoen, Pannika , et al. Development and Evaluation on Cybersecurity Behaviour Measurement Instruments for Undergraduate Students. 2024. International Journal of Cyber Criminology.