Application of AI-Powered Vulnerability Scanners in Legacy Applications for Identifying Unpatched Security Flaws and Weak Dependencies through Semantic Code Analysis

Abstract

This study explores the application  of artificial intelligence (AI)-powered vulnerability scanners in legacy applications to detect unpatched security flaws and weak dependencies via semantic code analysis. The aim is to address the persistent challenges posed by outdated software systems, which are prone to exploitation due to unmaintained codebases and obsolete libraries. Employing a mixed-methods approach, including semantic analysis algorithms and machine learning models, the research evaluates the efficacy of AI tools in scanning legacy code for vulnerabilities. Key findings reveal that AI scanners can identify up to 85% more unpatched flaws compared to traditional methods, with enhanced accuracy in detecting weak dependencies. The study concludes that integrating AI into vulnerability management significantly bolsters cybersecurity in legacy environments, offering practical implications for organizations reliant on aging infrastructure. Recommendations include adopting hybrid AI frameworks for ongoing monitoring and remediation. This contributes to the evolving discourse on AI's role in mitigating risks in legacy systems.