Abstract
Project Mentor(s): Hideki Takei, DBA
As cybersecurity threats evolve in complexity and scale, the reliance on artificial intelligence (AI) has become increasingly prevalent across both public and private sectors. This study examines the dual role of AI-driven predictive analytics in strengthening organizational cybersecurity, while addressing the ongoing need for human oversight. Through a mixed-method approach, combining survey data from cybersecurity professionals with an extensive literature review, this research analyzes AI's capacity to detect emerging threats, the systemic challenges associated with AI integration, and the indispensable role of human expertise in interpreting AI outputs. Findings indicate that while AI enhances proactive threat detection, its efficacy is limited by false positives, system incompatibility, and skill gaps within existing cybersecurity teams. This paper proposes the AI-Cybersecurity Optimization Framework (AICOF)as a strategic model to guide organizations in harmonizing automation and human expertise, fostering a more resilient cybersecurity posture. The findings underscore the importance of continuous learning, ethical oversight, and cross-disciplinary collaboration in ensuring the long-term success of AI adoption in cybersecurity. Presentation recording available in the SOURCE 2025 playlist: https://www.youtube.com/@cwusource5518
SOURCE Form ID
8
Recommended Citation
Dutong, Cathy Mae C.
(2025)
"Optimizing Cybersecurity Through AI Predictive Analytics and Human Expertise,"
Journal of the Symposium of University Research and Creative Expression: Vol. 1, Article 15.
Available at:
https://digitalcommons.cwu.edu/jsource/vol1/iss1/15