International Journal of Smart Sensor and Adhoc Network
A Quantitative Study of Risk Scores and the Effectiveness of AI-Based Cybersecurity Awareness Training Programs
Cybersecurity awareness training plays a dynamic role for organizations in certifying resources' accessibility. This paper determines the correlation between an employee's risk score and the effectiveness of AI-based security awareness training that deals with cyber threats. The research uses the Unified Theory of Acceptance and Use of Technology to update prior research, revealing that at-risk employees' behavior and information security awareness training implementation make up successful interventions. However, those studies did not discuss AI training, and so this research fills that literature gap. This study used a quantitative research design. The researcher analyzed survey responses using Pearson's Correlation and an independent t-test to determine statistically significant relationships and differences between employees' risk scores and an AI-based security awareness training programs' effectiveness. The calculations came from a sample of 200 participants from two different organizations. The Pearson product correlation of employee's risk scores and the effectiveness of the security awareness training program was statistically significant. The researcher also conducted an independent-samples t-test to compare the employees' risk scores by gender. There were no significant differences in scores. Male was higher than female ones. The mean difference was minimal. The findings herein help interpret the role of information security awareness training in the workplace, promoting behavioral changes that would impede data violations by including the users' vulnerability and the severity of intimidation, and the response to a threat in prognosticating behavior intentions.
Ansari, Meraj Farheen
"A Quantitative Study of Risk Scores and the Effectiveness of AI-Based Cybersecurity Awareness Training Programs,"
International Journal of Smart Sensor and Adhoc Network: Vol. 3:
3, Article 1.
Available at: https://www.interscience.in/ijssan/vol3/iss3/1
Digital Communications and Networking Commons, Electrical and Computer Engineering Commons