Why IaC Needs Extra Review
Infrastructure as code mistakes are expensive in a way that application code bugs usually aren't. A misconfigured security group can expose your database to the internet. An oversized instance type can cost thousands per month. A missing backup configuration means you discover the problem only when you need the backup and it's not there.
Traditional code review catches some of these issues, but reviewers often aren't infrastructure experts. They can verify the Terraform syntax is valid without knowing whether ingress from 0.0.0.0/0 on the database security group is a terrible idea. AI reviewers connected through MCP servers can flag these patterns consistently because they've been trained on infrastructure best practices.
What AI Catches in IaC
Security misconfigurations are the biggest win. Open security groups, unencrypted storage, missing logging, overly permissive IAM policies, publicly accessible resources that should be private. These patterns are well-known but easy to miss in a pull request with 200 lines of Terraform changes. An AI reviewer checks every resource against security best practices.
Cost optimization is another area where AI review helps. The reviewer can flag when someone provisions an m5.4xlarge for a service that historically uses 10% CPU, suggest reserved instances for long-running workloads, or point out that a NAT Gateway in every AZ might not be necessary for a dev environment. These suggestions save real money.
Best practice violations round out the coverage: resources without tags, missing descriptions on security group rules, hardcoded values that should be variables, and drift from your organization's infrastructure standards.
Setting Up the Review Workflow
The setup mirrors application code review but with infrastructure-specific rules. Connect your AI assistant to GitHub through an MCP server, configure it to trigger on PRs that modify .tf, .yaml, or .json infrastructure files, and give it a review checklist specific to your infrastructure standards.
Your review checklist might include items like: all security groups have explicit descriptions, no resources use default VPC, all S3 buckets have versioning enabled, all RDS instances have automated backups, and all resources have cost-center tags. The AI reviewer checks every item for every PR, something human reviewers struggle to do consistently.
Combining with Static Analysis
AI review works best alongside tools like tfsec, checkov, and OPA (Open Policy Agent). These tools catch known patterns with zero false positives. The AI catches nuanced issues that static rules can't express: "this security group allows SSH access from a broad range, which is fine for a bastion host but suspicious for an application server." The combination of static analysis and AI review creates a strong safety net.
You can feed static analysis results to the AI agent as context for its review. "tfsec found 3 HIGH severity issues" gives the agent a starting point, and it can add additional findings that tfsec's rules don't cover. Check the skills library for pre-built infrastructure review skills.
Related Reading
- AI-Powered Code Review with MCP
- Multi-Environment Deployments with AI
- Secret Management Across Environments
Browse MCP servers on Skillful.sh. Search 137,000+ AI tools.