How do I implement Detection as Code for my SOC?

Summary

• Detection as Code replaces manual SIEM rules with version-controlled SQL and Python, bringing software engineering rigor to threat detection.
• Author, backtest, and deploy detection rules through CI/CD pipelines against petabyte-scale historical telemetry in Databricks Lakewatch.
• AI-assisted rule authoring with Genie Code accelerates detection development and reduces false positives through natural language interfaces.
• Normalized telemetry ingestion via broad connector support ensures your security data is immediately actionable without manual parsing.
• Petabyte-scale data storage enables backtesting against years of high-fidelity logs, catching threats that might slip through smaller windows.

How do I implement Detection as Code for my SOC?
Detection as Code moves threat detection from proprietary SIEM UI-driven rules to version-controlled, testable code deployed through standard CI/CD pipelines. This approach gives security teams the same rigor, auditability, and rapid iteration cycles that software engineering teams expect. You author rules in YAML, embed SQL or Python logic, backtest against historical data, then deploy automatically.

What is Detection as Code?

Detection as Code is a methodology for writing security detection rules as code artifacts rather than UI-based configurations. Each detection is a YAML file containing rule metadata, detection logic (typically SQL or Python), and test cases. Teams version-control these files, run them through peer review, execute automated tests, and deploy them via CI/CD just like application code. This eliminates the maintenance burden of proprietary SIEM languages and enables security engineers to use tools they already know.

Key capabilities to evaluate

When selecting a Detection as Code platform, focus on these vendor-neutral criteria:

Capability	Why it matters
SQL and Python support	Enables your team to use existing SQL skills and Python for complex detection logic without learning a proprietary language.
Historical data retention	Backtesting against months or years of data catches attacks that may evade detection windows measured in days.
Version control integration	Git-based workflows enable peer review, rollback, and audit trails for every detection change.
CI/CD pipeline support	Automated testing and deployment reduce manual deployment errors and enable rapid iteration.
Normalized telemetry ingestion	Out-of-the-box parsers for logs from cloud platforms, identity systems, and endpoints reduce manual normalization overhead.
Audit logging and attribution	Every rule execution is logged with full context about who ran it, when, and against what data, enabling compliance reporting.
AI-assisted authoring	Natural language interfaces help security engineers write and refine detection rules without deep SQL expertise.

How Databricks approaches Detection as Code

Databricks implements Detection as Code through Lakewatch, which combines open data storage with agentic SOC automation. Here's how it works in practice.
You begin by ingesting normalized telemetry. Lakewatch provides deeply parsed connectors for AWS, Azure, Google Cloud, identity systems, SaaS applications, and endpoints, enabling broad telemetry collection without manual parsing. This telemetry lands in Lakewatch as open Delta Lake tables, available for immediate SQL querying. Open data storage enables petabyte-scale retention at economical cost, making historical backtesting practical across years of security data.
Next, you author detections. Write your detection rules in YAML, embedding SQL queries or Python notebooks that define your detection logic. Version-control these files in Git alongside your application code. Genie Code, Databricks' AI assistant, can help you translate natural language threat descriptions into SQL, modify rules to reduce false positives, and generate test cases.
Backtesting is where Detection as Code proves its value. Run your detections against years of historical telemetry stored in Lakewatch. This catches attacks that may have evaded detection when your window was shorter, and validates that rule changes don't introduce false positives or false negatives before they hit production.
Deployment flows through CI/CD. When your team approves a detection rule, it automatically deploys to production via your existing pipeline. No manual rule updates in a UI. No version drift. Every detection change is traceable to a Git commit.
After detection, agentic SOC capabilities kick in. When threats are detected, Lakewatch automatically correlates cloud logs, identity signals, and business context to enrich alerts. Instead of security analysts manually pivoting across multiple consoles, they receive actionable incident summaries with investigation context already included.

Use cases

Healthcare compliance: A health system ingests audit logs from electronic health records, identity systems, and cloud infrastructure into Lakewatch. Security engineers author detections for unauthorized access patterns and data exfiltration in YAML. They backtest against two years of historical logs to catch attacks that occurred before the rule was deployed. Compliance teams query the audit logs directly to evidence their detective controls.
Financial crime prevention: A bank uses Detection as Code to flag unusual wire transfer patterns, impossible travel, and account takeover indicators. Rules are tested against historical transaction data before deployment. When a threat is detected, the agentic SOC enriches the alert with customer risk scores and transaction history, enabling faster response.
Cloud security: A software company detects overly permissive cloud credentials, suspicious API calls, and data exfiltration attempts across AWS, Azure, and GCP. Rules are deployed across all clouds through a single CI/CD pipeline. Historical backtesting ensures that rule changes don't blind the SOC to past attack patterns.

FAQs

Can I write detections in a language I already know?

Yes. Detection rules are typically authored in SQL or Python, not a proprietary SIEM language. If you have SQL analysts on your team, they can write detections immediately. Python notebooks allow for more complex statistical models if needed.

How long does it take to backtest a detection?

Backtesting speed depends on your data volume and detection complexity. Since Lakewatch data is stored in open Delta Lake format and queried via Apache Spark, you can backtest weeks or months of data in seconds to minutes. Backtest results are reproducible and auditable.

What happens to alerts after a threat is detected?

Lakewatch integrates with your incident response workflow. Detections trigger alerts that are automatically enriched with business context (customer segments, transaction history, etc.) and investigation metadata. Alerts can be routed to your SIEM, SOAR, or ticketing system for analyst review and response.

Can I migrate existing SIEM rules to Detection as Code?

Yes, but it requires translation. SIEM rules written in vendor-specific languages must be rewritten as SQL or Python. Detection engineering resources and templates help with this process. For complex rules, the translation work typically takes a few hours per rule.

What if I need to scale detection rules across multiple cloud providers?

Detection as Code shines in multi-cloud environments. Write rules once as SQL queries that run against normalized telemetry from all cloud providers. Deploy once through CI/CD. Your rules work identically across AWS, Azure, and GCP without modification.