How can I safely review AI-generated SQL and Infrastructure as Code?

How can I safely review AI-generated SQL and Infrastructure as Code?

Safely reviewing AI-generated code is the process of verifying machine-generated scripts for logical accuracy, security vulnerabilities, and long-term maintainability using a combination of automated validation tools and human oversight. In our experience, this requires treating every Large Language Model (LLM) output as a contribution from a junior engineer who possesses infinite knowledge but zero context of your specific production environment.

According to the GitHub Octoverse 2024 report, 41 percent of code in modern repositories is now AI-assisted. While this speed is a massive boon for data teams, it introduces a significant trust barrier. When we deploy systems for our clients, we utilize a three-signal code audit framework: logic verification, security scanning, and maintainability checks. This ensures that a hallucinated SQL join or an over-privileged Terraform IAM role never makes it into a production environment.

The challenge is not just whether the code runs, but whether it runs correctly and securely. A SQL query that executes without errors can still return the wrong data by missing a filter or misinterpreting a relationship. Similarly, Infrastructure as Code (IaC) that successfully provisions a resource might simultaneously open a security hole. To address this, we implement specific gates that every piece of AI code must pass through before a human ever looks at it.

Why is auditing LLM generated SQL scripts different from manual code review?

Auditing LLM generated SQL scripts requires a shift in focus because AI models do not understand the underlying business logic of your data. A human engineer knows that your "active_users" table requires a specific filter for deleted accounts; an LLM only knows what it has seen in its training data or the limited schema context you provided in the prompt.

One common failure we see is the hallucination of table columns or relationships. The LLM might assume a standard naming convention like user_id when your legacy system uses u_id. Without a robust review process, these small discrepancies lead to broken pipelines. We recommend using dbt (data build tool) tests to validate the output of AI-generated SQL. By running dbt test on a staging version of the AI-generated model, you can instantly see if unique constraints or null relationships are violated.

Furthermore, AI models often struggle with non-deterministic behavior in SQL. For example, they might write a query that relies on an implicit sort order that is not guaranteed by your data warehouse, such as BigQuery or Snowflake. Our team treats these outputs as high-risk drafts. We require every AI-generated script to be formatted with sqlfluff to ensure it meets our style guides, and then we run it against a slimmed-down development dataset to verify the result sets match expected outcomes.

If you are struggling to maintain quality while your team adopts these tools, our AI Stack Audit can help you identify exactly where your governance is failing.

How do you build a secure review process for AI IaC?

Developing a secure review process for AI IaC is significantly more critical than SQL review because the blast radius of an infrastructure error is much larger. If an LLM generates a Terraform block that creates an S3 bucket, it might default to making that bucket public if the prompt was not sufficiently specific.

In our work with mid-market SaaS companies, we have found that AI models frequently suggest the "easiest" path to make something work, which often involves using broad permissions like AdministratorAccess or * in IAM policies. A secure review process must include static analysis tools that catch these patterns automatically. We use Checkov and TFLint as mandatory CI/CD gates. These tools scan the AI-generated Terraform or CloudFormation scripts for hundreds of common security misconfigurations before the code is even eligible for a Pull Request (PR) review.

Another essential step is the "Plan Review." For Terraform, this means always running a terraform plan and reviewing the proposed changes. We have seen instances where an LLM suggested a minor change to a resource property that, unbeknownst to the user, would trigger a full destruction and recreation of a production database. A human reviewer must look at the plan output to confirm that the AI-generated code is performing the intended action and nothing more.

What does automated validation of AI data code look like in production?

Automated validation of AI data code is the only way to scale AI-assisted development without hiring an army of reviewers. We believe in a "fail fast" approach where the machine corrects the machine.

For a modern data team, this pipeline typically looks like this:

1. Linter Gate: The AI-generated code is automatically formatted and checked for syntax errors using tools like sqlfluff for SQL or Prettier for general code.
2. Static Analysis Gate: For IaC, tools like Checkov scan for security violations. For SQL, dbt-checkpoint can ensure that the generated models have descriptions and follow project standards.
3. Simulation Gate: The code is deployed to a temporary, isolated environment. For SQL, this is a separate schema in BigQuery. For IaC, this might be a dedicated "sandbox" AWS account or GCP project.
4. Contract Testing: We run tests to ensure the output of the code matches a predefined "contract." If the AI is writing a new ELT (Extract, Load, Transform) script, the automated validation checks if the output table schema matches what the downstream BI tools expect.

By the time a human lead engineer receives a notification to review a PR, the code has already been vetted for syntax, security, and basic functionality. This allows the human to focus on high-level architecture and business logic rather than catching missing semicolons or public buckets. We teach these specific patterns in our Learn AI Bootcamp to help teams move from manual bottlenecks to automated flow.

How should you treat AI outputs during human Pull Request reviews?

When our team conducts a human review of AI-generated code, we follow a specific rubric. We do not assume the code is correct just because it passed the automated tests. Instead, we ask three specific questions:

First, is the logic "correct" or just "plausible"? AI is excellent at writing plausible code that looks right at a glance but contains subtle logical flaws. We look specifically at JOIN types and GROUP BY logic in SQL, and resource dependencies in IaC.

Second, does this code introduce "technical debt"? LLMs often generate verbose or redundant code to solve a problem. A human reviewer should look for opportunities to refactor AI-generated code into reusable modules or dbt macros. If the AI generates 50 lines of SQL that could be a 5-line macro, the human must intervene.

Third, does this code adhere to our specific environment context? The LLM does not know that your production database has a specific performance bottleneck or that you have a naming convention for your cloud resources. The human reviewer is the guardian of context.

What are the most common pitfalls when reviewing AI-generated code?

The most common pitfall is "automation bias," which is the tendency to over-rely on automated systems. If the code passes the linter and the tests, reviewers often skim the logic. In our experience, the most dangerous bugs are the ones that pass tests but fail the "business reality" check.

Another pitfall is providing too much trust to the model's explanations. Many developers use "Explain this code" features to help them review. However, the model will often provide a very confident explanation of why the code is correct even when the code is wrong. You must review the code itself, not the model's justification for the code.

Finally, teams often forget to update their documentation. When AI generates code, it rarely generates the corresponding documentation or metadata needed for a healthy data catalog. A secure review process must include a check for whether the new SQL models or infrastructure resources are properly documented in your internal systems.

Frequently Asked Questions About AI Code Review

Can I trust AI to write complex SQL window functions or recursions?

You can use AI to generate the initial structure of complex window functions, but you should never trust the output without manual verification. AI often struggles with the specific frame clauses (like ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) or the specific partitioning logic required for your data grain. We recommend writing a small set of "golden" test data with known results and running the AI-generated window function against it to confirm the math is correct.

How do I prevent LLMs from hallucinating Terraform providers or resource types?

The best way to prevent hallucination in IaC is to provide the LLM with the specific documentation or "context" of the provider version you are using. If you use a tool like Claude Code or GitHub Copilot, ensure it has access to your local .terraform.lock.hcl file. Furthermore, your automated validation of AI data code should include a terraform validate step, which will immediately catch any non-existent resource types or invalid provider configurations before they reach your review queue.

Should I use a separate environment for testing AI-generated infrastructure?

Yes, we strongly recommend a "sandbox" or "ephemeral" environment for testing AI-generated IaC. AI-generated code can occasionally attempt to create expensive resources or modify critical networking components. By testing in an isolated environment with strict spending limits and no connection to your production data, you can safely observe the results of the terraform apply without risking your core business operations.

How does AI-assisted development affect the speed of the data team?

In our experience, AI-assisted development can increase the output of a data team by 2 to 3 times, but only if the review process is automated. If a senior engineer has to manually review every line of code as if they wrote it themselves, the bottleneck simply shifts from "writing" to "reviewing." By implementing the automated validation of AI data code, you allow your senior talent to act as "editors" rather than "authors," which is where the real productivity gains occur.

Ready to secure your AI development workflow?

If your team is moving fast with AI but you are worried about the quality and security of your production data scripts, we can help. Our AI Stack Audit provides a comprehensive assessment of your current governance and gives you a roadmap to implement a secure review process for AI IaC and SQL.

We also offer hands-on training through our Learn AI Bootcamp, where we teach your engineers how to build the exact automated validation pipelines described in this post. Whether you are a startup founder looking to automate your reporting or a data leader scaling a team, we provide the technical foundation to ship AI-assisted code with confidence.

Want to discuss your specific code review challenges? Book a free consultation with our team to see how we can help you build a production-grade AI development environment.