Why AI-Generated Terraform and Kubernetes Configs Are Silently Wrong
Most platform engineers have a version of the same story: they asked an AI assistant to scaffold a Terraform module or a Kubernetes deployment manifest, it came back looking completely reasonable, the CI pipeline went green, and weeks later something bad happened. An IAM role with wildcard permissions. An S3 bucket that wasn't supposed to be public. A Kubernetes pod running as root because nobody checked the security context.
The core problem isn't that LLMs write bad syntax — they rarely do. The problem is that IaC correctness has almost nothing to do with syntax. A Terraform file that terraform validate accepts can still deploy a security disaster. A Kubernetes manifest that kubectl apply --dry-run=client accepts can still schedule pods with dangerous capabilities. The tools your CI pipeline uses to check the code are mostly checking the wrong things.
