Mixed-method analysis shows a 40% reduction in phishing susceptibility, indicating that adaptive learning can improve security awareness effectiveness.
Although human error is still the most critical of vulnerabilities, it has been estimated to contribute to more than 90% of all data breaches in the contemporary world of dynamic cyber security. The traditional security awareness training programs not have been sufficient in reaching out to the users owing to old-fashioned event such as static presentations and generic quizzes. This research instead proposes a novel Security Awareness Training System for improved engagement, retention, and promptness to reality threats. It employs adaptive learning procedures, interactive simulations such as phishing attacks, social engineering scenarios, and gamification to present an innovative and personalized training experience. With AI analytics undergirding the system, individual user activities are assessed, contents fitted against risk profiles, and real-time feedback sustained to reinforce secure practices. It was an evaluation mixed-method quantitative such as reduction, pre-post training assessment scores, and others-phishing susceptibility, qualitative user feedback-to measure effectiveness. Preliminary results suggest actual improvements in participants' security hygiene, 40% decrease in phishing click-through rates. They retained better knowledge over the long term than just with the old training methods. Moreover, the scalable architecture of the system allows most IT infrastructures in organizations, small or large, to easily adapt it to their environments. The research clearly illustrates weaknesses in existing training paradigms while providing a data-driven, user-centered framework facilitating entities' future cybersecurity education initiatives.
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Mwakanjuki et al. (2025) studied this question.
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