AI-Powered Penetration Testing Platform for Automated Vulnerability Detection: A Survey of Artificial Intelligence Methods for Identifying System Weaknesses

Abstract: The increasing use of shared code libraries, cloud platforms, and collaborative repositories has introduced serious security risks such as hidden vulnerabilities, leaked credentials, and insecure dependencies. Traditional penetration testing methods are often slow, manual, and difficult to integrate into modern DevSecOps workflows. This paper reviews recent AI-based vulnerability detection and penetration testing systems, analyzing their strengths and limitations. It also proposes an AI-powered autonomous security platform that combines static code analysis, dependency scanning, secret detection, Docker-based exploit verification, continuous GitHub monitoring, and AI- generated remediation guidance within a unified interface to improve repository security and vulnerability management.

Keywords: Penetration Testing, Vulnerability Detection, Repository Security, DevSecOps, Artificial Intelligence, Machine Learning, Secret Detection, GitHub Security, Exploit Verification, Docker Sandbox