Integration of AI Algorithms in the Vulnerability Testing Process

Abstract

Artificial intelligence's rapid development is changing cybersecurity, particularly in the areas of vulnerability assessment and detection. Scalability and accuracy issues plague traditional vulnerability testing techniques, which is why AI-powered solutions are starting to look like a desirable substitute. This paper examines the integration of AI algorithms into vulnerability assessment, focusing how they can improve mitigation techniques, highlight risks, and improve threat detection. We investigate how AI-driven methods can improve system vulnerability detection, lower false positives, and expedite reaction times using a realworld case study. The findings demonstrate how AI could transform risk assessment and improve the intelligence, speed, and adaptability of security solutions. CZU: 004.056:[004.83+004.89]; JEL: C63, D81, L86, O33