AI-Driven Cyber Threat Intelligence Automation
This addresses inefficiencies in CTI for industrial security, offering a vital advantage in dynamic threat landscapes, though it appears incremental as it builds on existing AI technologies.
This study tackled the problem of automating Cyber Threat Intelligence (CTI) processes in industrial environments, which traditionally rely on manual methods, by using GPT-4o and fine-tuning techniques, resulting in reduced manual effort while maintaining precision in CTI reports.
This study introduces an innovative approach to automating Cyber Threat Intelligence (CTI) processes in industrial environments by leveraging Microsoft's AI-powered security technologies. Historically, CTI has heavily relied on manual methods for collecting, analyzing, and interpreting data from various sources such as threat feeds. This study introduces an innovative approach to automating CTI processes in industrial environments by leveraging Microsoft's AI-powered security technologies. Historically, CTI has heavily relied on manual methods for collecting, analyzing, and interpreting data from various sources such as threat feeds, security logs, and dark web forums -- a process prone to inefficiencies, especially when rapid information dissemination is critical. By employing the capabilities of GPT-4o and advanced one-shot fine-tuning techniques for large language models, our research delivers a novel CTI automation solution. The outcome of the proposed architecture is a reduction in manual effort while maintaining precision in generating final CTI reports. This research highlights the transformative potential of AI-driven technologies to enhance both the speed and accuracy of CTI and reduce expert demands, offering a vital advantage in today's dynamic threat landscape.