The Intersection of Artificial Intelligence and Cybersecurity: Advancements in Threat Detection and Response

Abstract

The integration of Artificial Intelligence (AI) in the realm of cybersecurity has become increasingly important with the changing dynamics of cyberattacks. Contrary to conventional security measures that have largely relied on signature-based detection, the mounting complexity and frequency of cyberattacks justify the use of more advanced and dynamic means. AI, particularly through machine learning (ML) and deep learning (DL), is very promising in enhancing threat detection and response capabilities. These technologies support automated processing of large volumes of data, pattern identification, and prediction of new threats in real-time. However, despite such developments, there remain issues related to model accuracy, vulnerability of AI systems to adversarial attacks, and implementability of using AI-influenced security measures in heterogeneous environments. Moreover, the lack of interpretability of the AI model raises serious concerns about trust and accountability, especially in high-risk industries like finance, healthcare, and government. This research aims to fill the current gaps by exploring novel AI-centered strategies that improve threat detection, response effectiveness, and the overall security system resilience.