How to Secure AI-Generated Code: A Developer's Workflow

AI coding assistants can accelerate software delivery, but their speed and volume may amplify familiar security risks, including insecure patterns such as SQL injection and cross-site scripting, missing authentication or input validation, hardcoded secrets, hallucinated dependencies vulnerable to slopsquatting, and architectural flaws that conventional scanners may overlook. The recommended approach is to treat AI-generated code as untrusted until it passes standard security gates, while adding controls tailored to AI use: prompt tools explicitly for secure practices, run SAST, dependency, and secrets scans in both development environments and CI pipelines, verify all suggested packages, reserve human review for high-risk changes involving authentication, cryptography, sensitive data, or new endpoints, and prioritize findings based on whether vulnerable code is actually reachable and exploitable. The guidance also advocates lightweight governance defining permitted AI use, required scans, approval rules, and exception ownership, arguing that these measures can preserve development velocity while focusing security effort on the most consequential risks.

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