Enhancing IT Security with LLM-Powered Predictive Threat Intelligence

The primary objective of this research is to explore the integration of Large Language Models (LLMs) into IT security frameworks for enhancing predictive threat intelligence capabilities. Traditional threat detection systems often struggle with the dynamic nature of modern cyber threats. LLMs, with their advanced natural language processing abilities, present a novel approach by predicting and identifying potential security threats through pattern recognition and anomaly detection in vast datasets. This research employed a systematic methodology involving extensive data collection from diverse IT security logs and public threat databases. The selected LLM was trained on these datasets, focusing on recognizing linguistic and non-linguistic patterns indicative of potential security threats. Validation was conducted through a series of controlled tests comparing the LLM’s performance against traditional rule-based and machine learning models in simulated and real-world environments. The results demonstrate that the LLM significantly outperformed existing models in terms of detection speed, accuracy, and the ability to identify zero-day exploits. Notably, the LLM achieved an accuracy rate exceeding 95%, with substantial improvements in false positive reductions. The study concludes that leveraging LLMs in threat intelligence systems can profoundly enhance predictive capabilities and dynamically adapt to evolving cyber threats. This advancement not only bolsters security postures but also supports proactive security management, underscoring the critical role of LLMs in future IT security strategies.