Abstract

This paper examines current trends in vulnerability scanners based on threat intelligence powered by artificial intelligence. It explores how machine learning and deep learning, two types of artificial intelligence, enhance Qualys, OpenVAS, and Nessus, among other software packages. The report underlines the benefits of incorporating AI into real-time threat detection, contextual risk assessment, automated prioritization, and predictive analytics. Using research and case studies, it demonstrates how AI improves cybersecurity systems’ accuracy, scalability, and response time. The study also examines architectural and operational advances in vulnerability management enabled by automation and sensible decisions. Furthermore, it addresses challenges such as algorithmic bias, data privacy, and ethical considerations, encouraging greater research into transparent and safe AI for cybersecurity.