Automating Security Detection Rules with Claude Code Agents
In this episode, Tara showcases her sophisticated workflow for managing Datadog Cloud SIEM detection rules using Claude Code. She demonstrates how specialized sub-agents handle different aspects of the workflow: a detection rule writer that creates custom rules from natural language prompts, a validator that checks both query and Terraform syntax, and a tester that deploys test infrastructure in AWS to verify the rules actually work. The entire process, which includes writing Terraform modules, deploying to Datadog, creating test environments, and validating signals, is orchestrated through slash commands and takes about 10 minutes to complete end-to-end.
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Key Takeaways
- Specialized sub-agents can orchestrate complex multi-step workflows, from writing detection rules to deploying infrastructure and running tests
- LLMs have significantly improved at infrastructure as code, with Sonnet 4.5 showing high success rates (90%+) with Terraform syntax
- AI automation enables focusing on the 'idea' rather than the 'task' - defining what you want rather than how to implement it
- End-to-end testing automation (creating test infrastructure, emulating attacks, verifying detection) provides confidence in security rules
- Pointing AI agents to official documentation (API docs, Terraform provider docs) improves code quality and accuracy
Resources
Datadog Cloud SIEM
Security information and event management platform for threat detection and investigation
AWS Secrets Manager
Service for managing, retrieving, and rotating secrets throughout their lifecycle