AI Can Generate Your Infrastructure. Can Your CI/CD Pipeline Trust It?

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Ask a platform engineer how much of their Terraform, Kubernetes manifests or CI/CD YAML was drafted with an AI assistant, and the honest answer is usually “most of it.” Ask the same engineer how much of that code was security-reviewed before it shipped, and the answer gets quieter. This gap — between how fast AI writes infrastructure and how carefully anyone checks it — is no longer a theoretical risk. It’s showing up in production, in audits and now in a growing public CVE record. This isn’t about whether AI coding assistants are good or bad. They’re clearly useful, and they’re not going away. It’s about a specific, measurable failure mode that most CI/CD pipelines were never designed to catch, and what it takes to close that gap without slowing teams down to a crawl. The Numbers Are Worse for Infrastructure Than for Application Code IOActive’s April 2026 whitepaper, The Security Gap in AI-Generated Code, is the most rigorous test of this to date: 27 leading AI models, 730 real-world prompts spanning 27 languages, evaluated by 72 automated vulnerability detectors against roughly 20,000 generated code samples. The prompts deliberately didn’t mention security, because most developers don’t ask for it either. They just ask for working code, and that’s what they receive. The topline finding was bad enough on its own: Average security performance across all models came in at 59%, and nearly a third of generated samples were fully exploitable. Not one model configuration tested clean — even the best-performing setup produced 90 distinct vulnerabilities across the sample …

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