ARTIFICIAL INTELLIGENCE IN CYBERSECURITY RISK ANALYSIS ON NATIONAL VITAL INFRASTRUCTURE

Abstract

The development of digital technology has a significant impact on increasing cybersecurity threats, especially on national vital infrastructure such as the energy, transportation, and health sectors. Cyberattacks targeting these sectors have the potential to disrupt essential public services and threaten national security. Therefore, the use of Artificial Intelligence (AI) in cybersecurity risk analysis is an urgent need. This study aims to examine the effectiveness of AI in detecting and mitigating cyber threats on vital infrastructure. The method used is a mixed methods approach that involves quantitative analysis through questionnaires on the cybersecurity team and network log data analysis using the Isolation Forest and K-Nearest Neighbors algorithms. The results show that the application of AI can increase the speed of detection and effectiveness of threat mitigation, with anomaly detection accuracy reaching 95% and an odds ratio of 2.5 in cyber threat mitigation. These findings underscore that AI has a significant contribution to strengthening cybersecurity resilience on national infrastructure. However, some challenges such as integration with legacy systems and supporting regulatory needs need to be considered for further optimization.