Why AI code review is not enough (blog.codacy.com)

Recent discussions in the AI/ML community highlight the limitations of relying solely on AI-driven code reviews, prompting a shift in how engineering teams should handle AI-generated code. While AI-generated code appears polished and accurate, it can still harbor critical vulnerabilities that evade typical review processes. Studies reveal that as AI adoption increases, incidents of production failures relative to pull requests surge significantly, indicating that teams are merging code faster than their review workflows can adapt. This gap is particularly pronounced as AI-generated code may lack rigorous security assessments, suggesting the need for stricter review gates. To address this challenge, experts recommend implementing a structured approach that includes labeling AI-assisted pull requests and applying baseline security checks on all changes. This involves not only maintaining oversight from human reviewers but also reinforcing the system with deterministic checks for vulnerabilities, dependency risks, and static analysis. By treating AI output as a supplemental tool rather than a definitive authority, teams can enhance code safety and maintain a balanced workflow, ensuring that both speed and security are prioritized in their development processes.