LEVERAGING ARTIFICIAL INTELLIGENCE IN THREAT MODELING: ADVANCEMENTS, BENEFITS, AND CHALLENGES

Abstract

This article explores the transformative impact of artificial intelligence on threat modeling in cybersecurity. It begins by examining the fundamentals of threat modeling, including key methodologies and principles, before delving into the integration of AI-driven tools in this critical security practice. The article discusses various AI applications in threat modeling, such as automated threat identification, enhanced risk assessment, and mitigation recommendations. It highlights the significant benefits of AI-powered approaches, including increased efficiency, improved accuracy, and the ability to handle complex, large-scale systems. However, the article also addresses the challenges associated with implementing AI in threat modeling, such as data quality concerns, result interpretability, and the risk of over-reliance on automated systems. Looking ahead, the article explores future directions in AI-driven threat modeling, including the development of more sophisticated AI models, the integration of advanced machine learning techniques, and the potential for stronger synergies between AI systems and human security experts. Throughout, the article emphasizes the importance of balancing AI capabilities with human expertise to create more robust and comprehensive threat modeling practices in an increasingly complex digital landscape.