AI-Based Cybersecurity Training: A Quantitative Review

Quantitative Research Review for AI-Based Cybersecurity Awareness Training Programs Sushant Anil Patil Department of IT DSRT-734 Inferential Statistics in Decision Making Dr. Doug Bennett July 29 th , 2023

Quantitative Research Review for AI-Based Cybersecurity Awareness Training Programs Introduction: Cybersecurity is the most pivotal component of modern-day organizations in this highly digitized world. As such, there is a strong need for cyber empowerment of the general workforce. Organizations are leaving no stone unturned to provide awareness training to their staff to ensure security compliance and maintain standards (Dutta & Roy, 2008). The author, Meraj Ansari, 2022, in her article, looks into the connection between risk scores and the efficiency of these training courses. Organizations are spending more money on cybersecurity training as cyber threats continue to grow to reduce the risks that human mistakes could bring. This review aims to analyze the study's research design, outcomes, and implications critically. Methodology: a. Participants: Employees from diverse firms participating in the AI-based cybersecurity awareness training program were included in the study. b. Data Gathering: Before and following the cybersecurity training session, the researchers gathered information on the risk scores of the participants. Additionally, real-world security event data or simulated phishing tests may have been used to assess the training program's performance. c. Tools: The study employs the statistical analysis tool of correlation to process the data and evaluate correlations.

Findings: The success of the security awareness training program and the employee risk scores' Pearson product correlation exhibited a very weak positive correlation (r =.154, p .05), and the result is statistically significant at 0.05. As a result, people with more security training had more security awareness. The test also aimed to see if there was any difference between genders who received AI-based awareness training. There was no significant difference between the test scores of genders Male and Female. The risk scores for the employees' genders were compared using an independent-sample t-test. The scores did not differ significantly (t(198) =1.850, p=0.05). Male scores were higher than female ones (M=25.074, SD = 5.9022 vs. M=23.518, SD = 5.9522). However, there wasn't much difference in the means (1.5563, 95% CI: -.1026 to 3.2152). The tests also looked for a correlation between risk scores and duration of training. There was hardly any difference between the two fields, as suggested by its Pearson Correlation(p>0.5, r=-0.36). Limitations: The study's generalizability may be constrained depending on the characteristics of the individuals and the organizations involved. According to Bender & Cortés-Ciriano, 2021, the outcomes could be affected depending on the success and quality of the AI-based training program. The study might not have considered other variables affecting risk assessments, like other existing cybersecurity projects within the firms.

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Conclusion: In conclusion, the article offers insightful information on the connection between risk ratings and the efficiency of AI-based cybersecurity training programs. The study's findings imply that such training may help enhance cybersecurity awareness and lower firms' overall risk scores. However, given the study's limitations, additional research is required to confirm and improve these findings. To effectively support their cybersecurity efforts, organizations should carefully assess the efficacy and applicability of AI-based training programs within their specific contexts.

References: 1. Ansari, M. F. (2022). A quantitative study of risk scores and the effectiveness of AI-based Cybersecurity Awareness Training Programs. International Journal of Smart Sensor and Adhoc Network , 3 (3), 1. 2. Bender, A., & Cortés-Ciriano, I. (2021). Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet. Drug discovery today , 26 (2), 511-524. 3. Dutta, A., & Roy, R. (2008). Dynamics of organizational information security. System Dynamics Review: The Journal of the System Dynamics Society , 24 (3), 349-375.

Quantitative Research Review

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