Why You Should Use AI-Powered Terraform Plan Reviews in Your CI/CD Pipeline

As infrastructure-as-code becomes more complex, I wanted to create a systematic way to ensure every Terraform change meets security, cost, and compliance standards before reaching production.

One area that’s particularly ripe for improvement is Terraform plan reviews.

If you’ve ever spent half an hour combing through a terraform plan output, trying to spot subtle issues, you’ll know how easy it is for things to slip past even experienced reviewers.

That’s why I built an AI-powered GitHub Action that automatically analyses Terraform changes across 11 specialised domains before they reach production – consistent, comprehensive, and instant.

In this post, I’ll share why I built it, how it works, and how you can start using it in your CI/CD pipeline in just minutes.

Contents hide

1 What I Built (and Why)

2 Why AI Makes Terraform reviews better

2.1 Comprehensive Coverage Across 11 Domains

2.2 Instant Feedback on Every Pull Request

2.3 Works with ANY Terraform Provider

2.4 Customisable Analysis for your Workflow

3 Getting Started in Minutes

3.1 Add the GitHub Action to Your Workflow

3.2 Chose your AI Provider

3.3 Open a PR and see it in Action

4 HashiCorp MCP Integration

5 Real-World Benefits (From Production Use)

5.1 Proactive Issue Detection

5.2 Accelerated Code Reviews

5.3 Enhanced Team Learning

5.4 Consistent Standards Across Teams

6 Real Example: Before and After

7 Some Common Questions

8 Wrapping Up

What I Built (and Why)

As teams scale and infrastructure grows more complex, maintaining consistent review quality becomes challenging.

I wanted to create a solution that could systematically analyse every Terraform change with the same level of rigor, regardless of who’s available to review or when the PR is opened.

The GitHub Action I built does exactly that:

Traditional manual approach

Run terraform plan
Wait for an engineer to review the output
Review quality varies by reviewer and timing
Easy to miss subtle issues in complex plans

Manual Terraform reviews, while valuable, have inherent limitations that create opportunities for improvement:

1. Time-intensive – Senior engineers spending hours on review means less time for architecture and innovation

2. Variable consistency – Review depth naturally varies based on reviewer bandwidth and context

3. Complex pattern recognition – Identifying subtle security or cost patterns across hundreds of resources is challenging at scale

4. Scaling challenges – As infrastructure and teams grow, manual review becomes a bottleneck

Automated AI-powered approach

Run terraform plan and generate JSON output
AI automatically analyses across 11 specialised domains
Structured findings posted directly to the PR
Consistent, comprehensive review on every single PR

The result? Every infrastructure change gets expert-level analysis instantly – establishing a reliable baseline of quality that teams can build upon.

I saw an opportunity to use AI to augment human expertise – handling the systematic analysis while engineers focus on strategic decisions and business logic.

Do check it out here: https://github.com/thomast1906/terraform-review-ai-action

Why AI Makes Terraform reviews better

AI doesn’t replace your team’s expertise – it amplifies it. Here’s what makes AI-powered analysis so effective:

Comprehensive Coverage Across 11 Domains

The analysis covers areas that are easy to overlook manually:

Security – Exposed resources, missing encryption, overly permissive RBAC
Cost – Oversized instances, unnecessary resources, missed savings opportunities
Compliance – Regulatory requirements, organisational policies, governance rules
Performance – Resource sizing, scaling policies, network optimisation
Reliability – High availability, disaster recovery, fault tolerance
Observability – Logging, monitoring, alerting configurations
Networking – Security groups, firewall rules, connectivity issues
Data Protection – Encryption, backup strategies, storage optimisation
Best Practices – Provider-specific recommendations, infrastructure patterns
Deployment Safety – Breaking changes, data loss risks, rollback readiness
Governance – Tagging, naming conventions, resource organisation

This is the kind of checklist no one realistically runs by hand for every change — but AI can do it for every pull request

Instant Feedback on Every Pull Request

No more waiting for someone to review your Terraform changes. The moment you open a PR:

AI analysis runs automatically
Findings are posted as PR comments with severity levels (🔴 Critical, 🟡 Warning, 🔵 Recommendation, ✅ Good Practice)
Team sees exactly what needs attention before merging
Updates happen in the same comment (no spam)

This means faster iteration, faster deployments, and fewer surprises.

Works with ANY Terraform Provider

Whether you’re using AWS, Azure, GCP, Kubernetes, or any of the 1000+ community providers (Auth0, Datadog, MongoDB, Vault, etc.), the analysis adapts automatically.

The system:

Dynamically detects providers from your plan
Surfaces provider-specific best practices
Integrates with HashiCorp’s official Terraform MCP Server for real-time registry access
Provides tailored recommendations based on your infrastructure

No configuration needed – it just works.

Customisable Analysis for your Workflow

Not every review needs the same depth. You can customise analysis based on your needs:

Analysis Presets:

security-audit – Deep security and compliance review
cost-optimisation – Focus on cost savings and performance
production-ready – Comprehensive production deployment checks
quick-check – Fast security and best-practice scan for CI/CD
complete – All 11 domains analysed in detail

Analysis Depth:

standard – Balanced comprehensive analysis (~8K tokens)
quick – High-level critical findings (~4K tokens)
detailed – Exhaustive examination with learning opportunities (~12K tokens)

Analysis Modes:

plan-only – Fast analysis of just the Terraform plan JSON
comprehensive – Deep dive including source files for full context

This flexibility means you can run lightweight checks on every commit, and deeper reviews on production deployments.

Getting Started in Minutes

check it out here: https://github.com/thomast1906/terraform-review-ai-action

You can integrate this into your CI/CD pipeline with minimal setup.
Here’s the quickest route from “interesting idea” to “live in production”:

Add the GitHub Action to Your Workflow

name: Terraform AI Review
on:
  pull_request:
    paths: ['.tf', '.tfvars']

permissions:
  contents: read
  pull-requests: write
  models: read  # Required for GitHub Models

jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v5
      
      - uses: hashicorp/setup-terraform@v3
      
      - name: Terraform Plan
        run: |
          terraform init
          terraform plan -out=tfplan.binary
          terraform show -json tfplan.binary > tfplan.json
      
      - name: AI Review
        uses: thomast1906/terraform-review-ai-action@v1
        with:
          ai-provider: 'github-models'
          github-models-token: ${{ secrets.GITHUB_TOKEN }}
          github-token: ${{ secrets.GITHUB_TOKEN }}
          analysis-preset: 'production-ready'
          analysis-depth: 'detailed'

Chose your AI Provider

Two options, both work great:

Option 1: GitHub Models (Easiest)

Uses GitHub’s built-in AI models
No additional setup required
Just use ${{ secrets.GITHUB_TOKEN }}
Add models: read permission

Option 2: Azure OpenAI (More Control)

Use your own Azure OpenAI deployment
Control costs and model versions
Add secrets: AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_DEPLOYMENT

- name: AI Review
  uses: thomast1906/terraform-review-ai-action@v1
  with:
    ai-provider: 'azure'
    azure-openai-api-key: ${{ secrets.AZURE_OPENAI_API_KEY }}
    azure-openai-endpoint: ${{ secrets.AZURE_OPENAI_ENDPOINT }}
    azure-openai-deployment: 'gpt-4'
    github-token: ${{ secrets.GITHUB_TOKEN }}
    analysis-preset: 'production-ready'

Open a PR and see it in Action

That’s it. The next time you open a Terraform PR:

The action runs automatically
AI analysis appears as a PR comment
Team reviews the findings
Merge with confidence

HashiCorp MCP Integration

The action integrates with HashiCorp’s official Terraform Model Context Protocol (MCP) Server for enhanced validation:

What you get:

Real-time access to Terraform Registry documentation
Module recommendations from the registry
Resource validation against HashiCorp standards
Latest provider versions and compatibility checks
Direct links to official documentation

Enabled by default, but you can disable it if needed:

skip-mcp-validation: false

show-mcp-details: true # Show detailed MCP diagnostics

The MCP integration makes recommendations more specific and always up-to-date with the latest Terraform ecosystem.

Real-World Benefits (From Production Use)

Since deploying this action, I’ve observed significant improvements in infrastructure quality and team efficiency:

Proactive Issue Detection

The action systematically identifies opportunities for improvement:

Public access configurations that should be private
Encryption settings that can be enhanced
Resource sizing optimizations (regularly identifying $500+/month in savings)
Monitoring gaps that would impact observability
RBAC policies that can follow least-privilege principles more closely

All surfaced automatically during PR review – enabling teams to address them before deployment.

Accelerated Code Reviews

Review workflow becomes streamlined:

1. Review the AI-generated comprehensive summary

2. Focus discussion on critical findings and business logic

3. Approve with confidence in the baseline quality

Typical review time: 30+ minutes → under 5 minutes for standard changes.

Enhanced Team Learning

Every PR becomes a learning opportunity:

Specific explanations of findings with context
Actionable remediation steps with examples
Direct links to relevant documentation
Best practice patterns reinforced consistently

It creates a continuous learning environment where infrastructure knowledge compounds across the team.

Consistent Standards Across Teams

Every PR receives the same comprehensive analysis. Particularly valuable for:

Multi-team organisations maintaining unified standards
Distributed teams across different timezones
Teams with varying levels of Terraform expertise

Real Example: Before and After

Before (Manual Review):

> "Looks good to me 👍" _(merges PR with public S3 bucket)_

After (AI Review)

Critical Issues (🔴)
1. S3 Bucket Public Access - `aws_s3_bucket.data`
   - Issue: Bucket allows public read access via ACL
   - Risk: Sensitive data exposure, compliance violation
   - Fix: Set `acl = "private"` and use bucket policies for controlled access
   - Impact: HIGH - Potential data breach
   - Effort: 2 minutes

Warnings (🟡)
1. EC2 Instance Sizing - `aws_instance.web`
   - Current: t3.2xlarge (8 vCPU, 32GB RAM)
   - Recommended: t3.large (2 vCPU, 8GB RAM)
   - Savings: ~$180/month
   - Reason: CPU utilisation historically <15%

Recommendations (🔵)
1. Enable CloudWatch Monitoring - `aws_instance.web`
   - Add: detailed_monitoring = true
   - Benefit: Better visibility into instance health

Good Practices (✅)
1. Encryption enabled on all EBS volumes
2. Proper tagging applied (Environment, Owner, CostCenter)
3. Using latest AMI with security patches

See the difference? Specific, actionable, measurable.

Some Common Questions

Q: Does this replace manual code reviews?

No – it enhances them. Humans still review the logic and architecture. AI catches the security, cost, and compliance issues that are easy to miss.

Q: What if I use a niche Terraform provider?

It works with any provider. The system dynamically detects providers from your plan and adapts its analysis.

Q: Is my Terraform plan data secure?

Yes. Sensitive data is scrubbed before being sent to AI (passwords, API keys, secrets, etc.). You can also disable scrubbing if needed: `enable-data-scrubbing: ‘false’`

Q: What about costs?

GitHub Models uses your existing GitHub subscription (free for public repos, included with GitHub Enterprise). Azure OpenAI costs depend on your deployment and token usage (~$0.01-0.10 per review with GPT-4).

Q: Can I customise the analysis?

Absolutely. You can:

Edit system prompts in `prompts/` directory
Add company-specific policies
Adjust focus areas
Control output formatting

Wrapping Up

I built this action to bring systematic, comprehensive analysis to Terraform reviews – creating a baseline of quality that teams can rely on for every infrastructure change.

After months of production use, the value is clear: