THREAT MODEL DETECTION USING AI

Abstract

This paper explores the integration of Artificial Intelligence (AI) in bolstering threat model detection within cybersecurity frameworks, addressing the increasing sophistication of cyber threats that often outpace traditional security measures. By harnessing AI's capabilities for learning and adapting to new and evolving threat patterns, this study presents AI as a critical tool in augmenting cybersecurity defenses, offering a detailed analysis of AI methodologies in threat detection, including pattern recognition, anomaly detection, and predictive analytics. It critically evaluates the effectiveness of AI against conventional security approaches, highlighting the speed, efficiency, and adaptability of AI technologies. Furthermore, the paper navigates through the potential challenges AI faces, such as data privacy concerns, ethical implications, and the risk of adversarial attacks, while also forecasting future directions in AI-driven cybersecurity enhancements. This comprehensive examination underscores the transformative potential of AI in cybersecurity, urging for advancements in AI technology to stay ahead of cyber adversaries.