The AI-Driven Cyber Threat Analysis: A Survey

AI

Abstract

This paper explores the pivotal role of predictive analytics and its integration with artificial intelligence (AI) and machine learning (ML) in transforming cybersecurity practices and various industries. It focuses on how these advanced technologies enhance the ability to predict, detect, and prevent cyber threats, fostering more proactive defense strategies. The study delves into key techniques such as machine learning, natural language processing (NLP), and deep learning, which improve threat detection and intelligence. Through a comparative review of relevant research, the paper highlights the advancements in AI-driven predictive analytics, the challenges these technologies face, and the ethical concerns surrounding privacy, fairness, and transparency. Additionally, it explores the broad impact of predictive analytics across industries such as healthcare, finance, retail, and manufacturing, demonstrating its potential to optimize decision-making and operational processes. While the paper acknowledges the challenges of data quality, algorithmic bias, and scalability, it also outlines future research directions, including the integration of emerging technologies like quantum computing, the development of privacy-enhancing methods, and the establishment of sector-specific regulatory frameworks. In conclusion, the study underscores the transformative potential of predictive analytics in creating more secure, efficient, and adaptive systems across diverse domains in the future.