Demystifying SAST for IaC: How Does Checkov Actually Work Under the Hood?

RODRIGO SIDNEY COLQUE QUISPE — Tue, 21 Apr 2026 04:42:16 +0000

Abstract
This article dives deep into the internal architecture of Static Application Security Testing (SAST) tools focused on Infrastructure as Code (IaC). We break down step-by-step how Checkov parses source code, builds dependency graphs, and evaluates security policies to detect complex vulnerabilities before deployment.

In previous articles, we explored how to integrate tools like Checkov into our CI/CD pipelines using GitHub Actions. We know that if we feed it a Terraform or Kubernetes file, it magically returns a list of vulnerabilities.

But as engineers, we don't like magic. How exactly does Checkov know that an S3 bucket is misconfigured? Does it just look for the string "public-read" using Regular Expressions (Regex)?

The short answer is: no. Using Regex to analyze infrastructure would be a complete disaster due to the complex relationships between resources. Let's look at how modern Static Analysis actually works under the hood.

The Static Analysis Process (Step-by-Step)
The engine powering Checkov (and most advanced SAST tools) is written in Python and operates in three main phases: Parsing, Graph Construction, and Policy Evaluation.

1. Parsing and the AST (Abstract Syntax Tree)
Checkov's first challenge is that Infrastructure as Code comes in many "flavors": HCL (Terraform), YAML (Kubernetes/CloudFormation), JSON, etc.

Checkov takes your source code and uses language-specific parsers. The goal of this parser is not to look for errors just yet, but to transform your plain text code into a standardized data structure in memory—typically a Python dictionary or an Abstract Syntax Tree (AST).

# Original Terraform Code resource "aws_s3_bucket" "my_bucket" { bucket = "sensitive-data" acl = "public-read" }
2. Resource Graph Construction (Graph Database)
This is where the tool truly shines. In the cloud, resources are almost never isolated. A Security Group connects to an EC2 instance, and that instance connects to a Database.

If Checkov evaluated files line by line, it would lose all this context. To solve this, it uses a Python library called NetworkX to build a directed graph in memory.

Nodes: Represent the resources (e.g., the S3 bucket, the IAM user).
Edges: Represent the relationships between them (e.g., "User X has permissions on Bucket Y").

3. Policy Evaluation
Once the code has been converted into a comprehensible graph, Checkov begins running its "Policies" against it.

Policies in Checkov are small scripts written in Python (or defined in YAML). These rules scan the node attributes in the graph looking for specific risk patterns.

For example, the internal logic of a Checkov policy for AWS S3 would conceptually look like this:

The Graph-Based Engine: Why is it crucial?
Imagine a complex scenario: You have an S3 bucket that is set to private, but in a completely different file in your project, you create an IAM policy that grants public access to that specific bucket.

If Checkov used Regex or read files in isolation, it would report the bucket as secure. However, because it built a graph connecting the IAM policy to the Bucket via cross-references, the analysis engine can traverse the nodes and detect the compounded vulnerability.

Conclusion
SAST tools like Checkov are much more than simple linters looking for keywords. They are fully-fledged code analysis engines that build relational models of our infrastructure in memory to apply logical rules.

Understanding this workflow doesn't just help you appreciate the engineering behind DevSecOps tools; it opens the door to the next level: writing your own custom Python policies to audit specific business rules your team needs to enforce.

Implementing SAST in Your Infrastructure: Detecting Vulnerabilities with Checkov and GitHub Actions

RODRIGO SIDNEY COLQUE QUISPE — Tue, 21 Apr 2026 03:50:44 +0000

Abstract
This article explores the implementation of Static Application Security Testing (SAST) for Infrastructure as Code (IaC) using Checkov. We demonstrate how to identify common security misconfigurations, such as publicly accessible S3 buckets, and seamlessly integrate the scanning process into a CI/CD pipeline using GitHub Actions.

What is Checkov?
Checkov is an open-source static analysis tool (maintained by Bridgecrew / Prisma Cloud) designed specifically for Infrastructure as Code.

Unlike tools aimed at application code (like Bandit for Python), Checkov scans configuration files for misconfigurations that could lead to security vulnerabilities or compliance issues.

Why use Checkov?

Built-in policies: It comes with hundreds of out-of-the-box policies covering AWS, Azure, and GCP best practices.
Multi-framework: Supports Terraform, CloudFormation, Kubernetes, Dockerfiles, Serverless, and more.
Easy integration: Runs from the command line or directly in your CI/CD pipelines.

The Scenario: Vulnerable Infrastructure

Imagine you have a Terraform file (main.tf) where you define an S3 bucket. By mistake (or for a quick test), you configure it to have public read access:

# main.tf resource "aws_s3_bucket" "my_vulnerable_bucket" { bucket = "my-dev-test-bucket" acl = "public-read" # ❌ Security risk! }

If we deploy this, anyone on the internet could access our data. Let's make our repository catch this error before it gets merged into the main branch.

Automating with GitHub Actions
The real magic happens when we integrate Checkov into our CI/CD workflow. This way, every time someone pushes code or opens a Pull Request, Checkov will analyze the infrastructure automatically.

In your repository, create a file at .github/workflows/checkov.yml and add the following:

What exactly does this workflow do?

1. Checkout: Clones your code into the GitHub Actions virtual environment.
2. Run Checkov: Uses the official Bridgecrew action. The directory: . parameter tells it to look for infrastructure files throughout the repository.
3. soft_fail: false: This is the key to DevSecOps. If Checkov finds a failing policy (like our public bucket), the pipeline will fail, preventing vulnerable code from being integrated.

Conclusion
Shifting security to the left (Shift-Left Security) by implementing SAST in your Infrastructure as Code is no longer optional. With tools like Checkov and GitHub Actions, it's a fast and highly effective process. With just a few lines of code, you can ensure your team doesn't accidentally deploy insecure configurations.

DEV Community: RODRIGO SIDNEY COLQUE QUISPE

Demystifying SAST for IaC: How Does Checkov Actually Work Under the Hood?

Implementing SAST in Your Infrastructure: Detecting Vulnerabilities with Checkov and GitHub Actions