Adopting Detection-as-Code: Transforming Cloud Security Operations with a Proactive Approach

As cloud infrastructure evolves, so do the complexities of keeping it secure. Gone are the days when manual processes and reactive responses could suffice, and today’s cloud environments demand sophisticated, automated approaches.

One solution that stands out is Detection-as-Code (DaC). This approach treats threat detection logic and security operations as code, allowing organizations to harness the best practices of software engineering—like version control, consistency, and automated workflows—in their security operations. Here’s how implementing Detection-as-Code can help organizations streamline security, reduce noise, and achieve real-time responsiveness.

Why Detection-as-Code Matters

Traditional methods of security operations often become bottlenecks, especially as an organization’s infrastructure scales. Detection rules can quickly proliferate, become inconsistent, and lose their relevance as environments change and security threats evolve. By treating detection rules as code, organizations can centralize rule management, use version control, and automate deployment, ensuring uniformity and reducing human error.

The shift to Detection-as-Code usually addresses several critical issues:

1. Version Control: Defining detection rules in code makes it easier for multiple teams to collaborate, review, and evolve threat detection logic in a coordinated way.
2. Consistency and Approval: Detection-as-Code enables uniformity in detection logic, reducing gaps in security coverage by standardizing rules and ensuring they meet quality standards before going live.
3. Scalability: As companies grow, the volume and complexity of security data increase. By defining rules as code, organizations can perform bulk edits and redeployments effortlessly across multiple environments.

Crafting Detection Rules: The Infrastructure-as-Code Approach

We can use Terraform with Datadog to define and deploy detection rules across environments, treating them as code-based resources within Datadog’s Cloud SIEM, Application Security Management (ASM), and Cloud Security Management (CSM). With Terraform’s infrastructure-as-code capabilities, detection rules are created once and deployed across multiple customer organizations, ensuring consistency while preventing unwanted “drift” from the baseline state.

In this setup:

• Rules are defined in one place, enabling selective deployment across organizations.
• Drift prevention ensures alignment between rules defined in code and their real-time application.
• Granular control allows Datadog to specify environments for each rule, ensuring only relevant detections trigger alerts.
  
  

Structuring the Detection Rule Repository

For managing detection logic at scale, a good practice is to organize detection rules in a structured repository, divided into directories for rules, organizations, and tests:

• Rules Directory: Each rule is saved in a Terraform file and categorized by the product type (department, area, or organization) and data source (e.g., Kubernetes, GitHub, Azure).
• Organizations Directory: Each organization has a Terraform backend configuration that defines which rules apply in each environment.
• Tests Directory: End-to-end tests validate detection rules using simulated events. Tools like Stratus Red Team and Threatest can be used here for automated testing.

This approach allows detection engineers to isolate issues within specific environments, preventing disruptions to other deployments. Additionally, individual rules not needed in particular environments can be specified to exclude them, reducing noise and keeping alerts relevant.

Detection-as-Code in Action: Example of a Kubernetes Rule

One example in the Detection-as-Code framework is a rule for Kubernetes that detects failed attempts to access secrets. This detection rule leverages Datadog’s Cloud SIEM to monitor logs and flag potential issues based on conditions set in Terraform.

The rule includes:

• A query to identify suspicious access patterns, using predefined fields like the Kubernetes namespace and user ID.
• Severity settings for alerts, ensuring that only repeated access attempts trigger signals, reducing false positives.
• Suppression rules to exclude legitimate activity, filtering out alerts from known service accounts, reducing alert fatigue.

This setup ensures that engineers receive high-quality alerts, focusing their attention on real threats rather than sifting through false alarms.

Integrating Tests in the CI/CD Pipeline

Using CI/CD pipelines (e.g., GitLab, GitHub actions, etc.), detection rule testing and deployment can be automated. Each pipeline stage checks for syntax, quality, and deployment readiness, creating a seamless, reliable flow from development to production.

The CI/CD pipeline stages include:

1. Linting: Validates the code syntax and enforces tagging, ensuring consistency across rules.
2. Testing: Runs detection rules in a sandbox environment, generating alerts on test data to confirm rule accuracy.
3. Synthetic Logs: Tests detection logic using sample log data, ensuring the rules capture intended events.
4. Deployment: When rules are approved, they’re deployed across customer organizations, with scheduled checks to detect any rule drift or tampering.

The automated pipeline also generates JSON files that map rule coverage to MITRE ATT&CK tactics, providing security teams with a visual representation of their detection strategy.

Managing Detection-as-Code Changes

A good approach to Detection-as-Code begins with a log query, which helps engineers identify patterns or actions they’d like to detect. Once they’ve fine-tuned the query, it’s transformed into a Terraform rule and tested in a sandbox environment. Approved rules are then merged into the main repository branch, triggering automated deployments across multiple instances.

This automated development flow streamlines rule management, reduces manual errors, and ensures real-time deployment across multiple environments. It also allows teams to focus on adapting and improving detection logic, rather than being bogged down by routine management.

Detection-as-Code: A Model for Agile, Scalable Security

In the face of evolving cyber threats and increasingly complex cloud infrastructure, Detection-as-Code offers organizations a modern, resilient approach to cloud security. By adopting the best practices of software engineering—version control, automation, and modular rule definition—security teams can tackle threats with confidence and agility.

Implementing Detection-as-Code, backed by tools like Datadog’s Cloud SIEM, ASM, and CSM, shows how organizations can take control of their detection frameworks, keep pace with rapid changes in their cloud environment, and maintain a state of readiness.

Conclusion

Detection-as-Code is redefining how organizations manage and deploy threat detection. By embracing automation, testing, and scalable rule management, businesses can ensure their security practices keep pace with modern cloud environments. For AWS users and other cloud-native organizations, adopting Detection-as-Code is not just a best practice—it’s a competitive advantage in staying one step ahead of cyber threats.

Whether you're a newcomer to Detection-as-Code or looking to optimize your security processes, this approach can transform how you handle threat detection in complex cloud ecosystems.