More developers are using AI to generate code for applications, and the trend is creating new security risks. This follows the same pattern seen with open source: teams rarely write every line from scratch. Building everything by hand is slow and may introduce more vulnerabilities than it removes. Instead, programmers rely on existing libraries—often open source projects—for common functionality and basic components.
AI tools speed up that process by supplying ready-made snippets and whole modules. Those suggestions sometimes repeat insecure patterns, reference outdated dependencies, or introduce subtle logic errors that slip past tests. If output from these systems is merged into a product without strict review, critical bugs or supply-chain weaknesses may be introduced into production.
Open source dependencies have long posed similar threats when packages are compromised or when unsafe code is accepted unchecked. Static analysis, dependency scanners, and automated testing catch many mistakes, but they do not find every context-specific flaw. Human code review, threat modeling, and strong review policies remain important lines of defense.
Development teams should treat AI-produced code like any third-party contribution: require provenance, enforce test coverage, and sign or lock dependencies where possible. Training engineers to spot suspicious patterns and running targeted security audits will reduce the odds that AI suggestions become a vector for serious exploitation. The convenience of AI will push wider adoption and make rigorous controls a practical necessity for safe software development. Security teams should set formal policies for accepting AI suggestions and keep records of approvals. Those logs help auditors track changes and assign responsibility.