Reducing Software Vulnerabilities Using Machine Learning Static Application Security Testing

Manual code reviews have been used for as long as software development has existed. As modern software development practices continue to evolve so does the security testing of code. Static Application Security Testing (SAST) can play a significant role in helping developers identify defects in their code during the secure software development lifecycle. SAST tools have become more automated, support more languages, rely less on the developer’s knowledge, and are being considered by some to be an integral part of the industry’s collective best practices. Machine Learning and artificial intelligence are becoming integrated into these tools to detect vulnerabilities faster and with better accuracy. This paper compares manual code review, traditional SAST tools, and SAST tools with machine learning and artificial intelligence integrated to provide a starting point for organizations to choose the most appropriate code analysis technique for identifying potential vulnerabilities in their software.