Beyond the Hype: Using AI to Generate and Test Rego Policies in 2025

Can an AI *really* write the complex, nuanced policies needed for enterprise governance? The short answer is no—but it's becoming an incredibly powerful co-pilot that can accelerate development, improve quality, and lower the barrier to entry for policy authoring.

In 2025, the role of AI in policy-as-code isn't about replacement; it's about augmentation. Let's explore three practical, real-world use cases for using Large Language Models (LLMs) to write and manage OPA Rego policies today.

📋 Table of Contents

🤔 Use Case 1: Translating Plain English to Boilerplate Policy

One of the biggest hurdles with Rego is its unique syntax. This is a perfect task for an AI. By providing a clear prompt, you can generate a solid starting point that an expert can then refine.

"Write a complete OPA Rego policy. The policy should deny any AWS S3 bucket that does not have `versioning.enabled` set to `true`. The default decision should be `allow = true`."

package aws.s3.versioning
import future.keywords.if
default allow := true

deny[msg] if {
    input.aws_s3_bucket
    not input.aws_s3_bucket.versioning.enabled == true
    msg := "S3 bucket versioning is not enabled."
}

📝 Use Case 2: Explaining and Documenting Existing Policies

AI is an excellent tool for reverse-engineering and explaining existing code, dramatically reducing cognitive load for your team.

"Explain this Rego rule in plain English."

# Rego snippet to explain:
deny[msg] if {
   some container in input.spec.containers
   not startswith(container.image, "gcr.io/my-trusted-registry/")
   msg := sprintf("Image '%s' is from an untrusted registry.", [container.image])
}

🧪 The Killer App: Generating High-Quality Test Cases

Perhaps the most powerful use case for AI in policy development is generating test data. Writing comprehensive tests is critical but tedious. AI excels at this.

"Given this Rego policy, generate two JSON test inputs: one that should pass and one that should fail."

# Rego policy for context:
deny[msg] if {
   input.kind == "Namespace"
   required_labels := {"owner", "cost-center"}
   provided_labels := {label | input.metadata.labels[label]}
   missing_labels := required_labels - provided_labels
   count(missing_labels) > 0
   msg := "..."
}

// Test Case 1: Should PASS
{ "kind": "Namespace", "metadata": { "name": "prod", "labels": { "owner": "x", "cost-center": "y" } } }

// Test Case 2: Should FAIL
{ "kind": "Namespace", "metadata": { "name": "test", "labels": { "owner": "z" } } }

⚠️ Risks and Limitations: Why You Still Need an Expert

While AI is a powerful accelerator, it is not a replacement for human expertise. Relying on it blindly is a significant risk.

• Hallucinations: AI can confidently generate code that is subtly wrong or contains deprecated syntax.
• Security Vulnerabilities: Generated code is not guaranteed to be secure and might miss important checks.
• Lack of Context: AI doesn't understand your organization's specific threat model or business logic.