Examine the Role of Generative AI in Enhancing Threat Intelligence and Cybersecurity Measures

Abstract

GenAI is rapidly changing the way we do cybersecurity by automating the process of analyzing threats,
forecasting possible attacks, and enhancing our ability to respond to incidents when they occur. Traditional methods of
cyber defense rely heavily on the use of two types of detection methods: signature-based detection methods and rulebased
detection methods. Unfortunately, these types of detection methods are usually insufficient on their own when
dealing with zero-day vulnerabilities, APTs, or malware variants that change often. This paper proposes a threat
intelligence framework that utilizes generative AI to enhance the ability to defend against cyberattacks in real-time. It
includes a combination of LLMs as well as deep learning anomaly detection models and utilizes automated orchestration
of security tools. The generative AI framework collects threat intelligence from multiple data sources, including network
logs, vulnerability databases, malware signatures and as well as behavioral indicators. Threat intelligence is then
processed using a hybrid generative AI model to provide meaningful insights into potential future security threats as well
as automated mitigation strategies. Testing performed on simulated enterprise networks and standard cybersecurity
datasets shows a 97.8% accuracy rate for threat detection as compared to traditional machine learning models, a 62%
improvement in detection response time, and significant improvements in identifying and detecting zero-day threats. In
addition, an experiment that was performed on Random Forest, support vector machine and XGBoost to compare the
performance of generative AI-based threat intelligence models with other machine learning models shows generative AI
has superior contextual understanding of threats as well as superior predictive ability with respect to threats. Finally,
explainable AI techniques such as SHAP and attention visualization provide ways to explain how the models arrived at
their predictions