With the rapid acceleration of the Fourth Industrial Revolution, cyber threats have become increasingly frequent and complex. However, most public and private institutions still rely heavily on manual cybersecurity operations, which often lead to delayed responses and human errors, exposing critical vulnerabilities. A particular challenge lies in the inability to efficiently integrate and automate the analysis of threat logs collected from various sources, limiting the effectiveness of threat prediction and mitigation. To address these challenges, this study proposes an AI-based RPA (Robotic Process Automation) system designed to automate the collection, analysis, and dissemination of cyber threat logs. By minimizing human intervention, the proposed system significantly enhances real-time response capabilities and reduces errors. Additionally, standardizing and centralizing diverse log formats lays a foundation for the future development of AI models capable of predicting cyberattack patterns. This system is particularly well-suited for government and public organizations, offering a cost-effective solution that enhances cybersecurity while maintaining compatibility with existing infrastructures. The experimental results demonstrate that the proposed AI-based RPA system outperforms traditional manual systems in terms of log processing speed, prediction accuracy, and error reduction. This study highlights the critical role of automated AI-driven systems in enabling real-time threat response and prevention, presenting a practical and scalable approach for modern cybersecurity environments.