Whether you call it application security, product security, or DevSecOps, securing software is complicated. Today, practitioners are expected to manage a growing set of scanners, reduce large vulnerability backlogs, coordinate remediation across teams, and participate in architecture and threat modeling, often with limited headcount and little tolerance for noise.
AI is adding to this complexity, amplifying both the risks and opportunities in application security. AI assisted coding is reshaping how applications are built, deployed, and maintained. In parallel, the capabilities of platforms themselves are evolving with AI: features from autofix workflows, to false positive analysis, to scanning itself, are all radically changing product expectations.
This report is designed to help practitioners and buyers navigate the current application security landscape. It covers the transitions in application security over time, from waterfall development to DevOps to emerging AI code generation workflows. The report then breaks down every subcategory of scanner, the development of modern features, as well as how AI capabilities are changing functionalities we use today. We conclude with actionable buyer guidance that spans across SMB, mid-market, and enterprise environments.