Securing Cloud Resources with Policy as Code and Scanning

In the traditional data center era, security was often a physical or network-level perimeter concern that specialized teams handled after the hardware was racked and stacked. Modern cloud infrastructure has fundamentally changed this dynamic by representing entire networks and servers as software artifacts. This shift means that a single line of configuration can now expose a database to the public internet or grant overly broad permissions to a service account.

Infrastructure as Code allows engineering teams to deploy resources at a velocity that manual security reviews simply cannot match. When your deployment pipeline moves from a monthly release cycle to several deployments per hour, the security team becomes a bottleneck if they rely on manual audits. This disconnect creates a high risk of misconfigurations reaching production environments where they become much more expensive and difficult to remediate.

The most effective way to address this challenge is to treat infrastructure code with the same rigor as application source code. By implementing automated security scanning early in the development lifecycle, you enable developers to identify and fix vulnerabilities before they are ever provisioned in the cloud. This practice is often referred to as shifting left because it moves security considerations to the earliest possible stage of the software development life cycle.

Securing infrastructure at the source is not just a safety measure; it is a prerequisite for maintaining engineering velocity in a distributed cloud environment.

Automated scanning tools act as a continuous feedback loop for developers, providing immediate context on why a specific configuration is risky. Instead of waiting for a security incident report, an engineer receives a warning directly in their terminal or pull request. This immediate feedback helps build a stronger security culture within the engineering organization over time.

Static Analysis Security Testing for infrastructure works by parsing your definition files into an Abstract Syntax Tree or a graph representation. The scanner then runs a series of checks against this representation to find patterns that match known insecure configurations. For example, it might look for an S3 bucket resource that does not have an explicit server-side encryption block or an IAM policy that uses a wildcard in the action field.

Checkov is a popular Python-based tool that supports multiple providers including Terraform, CloudFormation, and Kubernetes manifests. It uses a policy-as-code approach where rules are defined in Python or YAML, allowing for complex logic that goes beyond simple regular expression matching. This allows the tool to understand relationships between resources, such as whether a security group is actually attached to a running instance.

On the other hand, tfsec is a Go-based scanner specifically optimized for Terraform that is known for its extreme speed and deep integration with the HashiCorp Configuration Language. It excels at identifying issues in nested modules and complex variable interpolations that might trip up simpler tools. Both tools provide a comprehensive library of built-in checks based on frameworks like the CIS Benchmarks.

1resource "aws_ebs_volume" "data_storage" {
2  availability_zone = "us-west-2a"
3  size              = 40
4  # Missing encrypted = true attribute
5  # Missing kms_key_id attribute
6
7  tags = {
8    Name = "database-storage"
9  }
10}
11
12resource "aws_security_group" "web_server" {
13  name        = "web-sg"
14  description = "Allow all inbound traffic"
15
16  ingress {
17    from_port   = 0
18    to_port     = 0
19    protocol    = "-1"
20    cidr_blocks = ["0.0.0.0/0"] # This is a major security risk
21  }
22}

The example above highlights two common misconfigurations: an unencrypted storage volume and a security group that allows all traffic from any source. A static analysis tool would flag these resources immediately during a scan. It would provide a specific error code and a description of the risk, such as the potential for data exposure or unauthorized network access.

The true value of infrastructure scanning is realized when it is integrated into the automated workflows that developers use every day. By adding a scanning step to your CI/CD pipeline, you can prevent insecure code from ever being merged into your main branch. This creates a hard gate that enforces security standards without requiring manual intervention from a security engineer.

A typical integration involves running the scanner during the linting or testing phase of your build. If the scanner finds a high-severity issue, it exits with a non-zero status code, which triggers the pipeline to fail. This forces the developer to address the security concern before they can proceed with their deployment.

1name: IaC Security Scan
2
3on:
4  pull_request:
5    branches: [ main ]
6
7jobs:
8  checkov-scan:
9    runs-on: ubuntu-latest
10    steps:
11      - name: Checkout code
12        uses: actions/checkout@v3
13
14      - name: Run Checkov
15        uses: bridgecrewio/checkov-action@master
16        with:
17          directory: terraform/
18          framework: terraform
19          soft_fail: false # Exit with error if issues are found
20          output_format: cli,sarif

In the workflow configuration above, the scanner is targeted at the directory containing Terraform files and configured to fail the build on any findings. The output is generated in both CLI format for easy reading in logs and SARIF format for integration with security dashboards. This setup ensures that every pull request is automatically audited for compliance with your security policies.

• Pre-commit hooks: Run scans locally before code is even pushed to the repository.
• Pull Request comments: Automatically post scan results as comments on the PR for better visibility.
• Failure thresholds: Define which severity levels (e.g., Critical, High) should block a build versus just showing a warning.
• IDE Plugins: Use extensions for VS Code or IntelliJ to see security warnings in real-time while writing code.

Setting up pre-commit hooks is particularly effective because it reduces the feedback loop to mere seconds. When a developer tries to commit code, the hook runs a lightweight scan and blocks the commit if any violations are found. This prevents the developer from context switching and keeps the remote build history clean of trivial security errors.

No automated tool is perfect, and there will be scenarios where a flagged configuration is actually intentional and secure. For example, a public S3 bucket might be required for hosting a static website, or a specific security group might need to be open for a public load balancer. Handling these exceptions cleanly is vital to maintaining a usable and trusted scanning system.

Most tools allow you to suppress specific checks using inline comments directly in the code. This ensures that the documentation for the exception lives right next to the resource it applies to. When a developer suppresses a check, they should always include a comment explaining why the exception is necessary and who approved it.

1resource "aws_s3_bucket" "public_assets" {
2  bucket = "my-company-public-assets"
3
4  # checkov:skip=CKV_AWS_18: "Bucket must be public for static asset delivery"
5  # tfsec:ignore:aws-s3-no-public-access
6  acl    = "public-read"
7
8  tags = {
9    Environment = "production"
10  }
11}

The example shows how to bypass specific checks for both Checkov and tfsec using resource-level comments. This approach is superior to globally disabling a check because it maintains high security for all other resources while allowing for local deviations. It also provides an audit trail within your version control system for all security decisions.

It is also important to establish a clear process for reviewing and auditing these suppressions periodically. If a team is skipping critical security checks without valid reasons, it undermines the entire security posture. You can use global policy files to restrict which checks are even allowed to be suppressed by individual developers.

As an organization grows, individual tool configurations can become difficult to manage across hundreds of repositories. Moving toward a Policy-as-Code framework like Open Policy Agent can provide a more unified approach to governance. OPA allows you to write policies in a language called Rego, which can be applied to any JSON-serializable input, including Terraform plans and Kubernetes manifests.

Scaling security also requires shifting from a reactive mindset to a proactive one by providing developers with secure-by-default modules. By creating a library of pre-approved Terraform modules that have already passed all security scans, you make it easier for developers to do the right thing than the wrong thing. This reduces the cognitive load on developers and ensures high-quality infrastructure from the start.

Finally, continuous monitoring of the actual cloud environment is necessary to catch drift or manual changes made outside of the IaC workflow. While static analysis catches issues in code, runtime scanners ensure that the deployed state matches the intended secure state. Combining these two approaches provides a defense-in-depth strategy for modern cloud environments.

The goal of automated security is not just to block bad things, but to enable engineers to move faster with confidence by providing them with a paved road of secure defaults.

Ultimately, the success of an IaC security program is measured by the reduction in production vulnerabilities and the speed at which developers can resolve findings. By integrating tools like Checkov and tfsec into the daily developer workflow, you turn security from a hurdle into a standard part of the engineering process. This alignment between security and development is the cornerstone of a mature DevOps practice.