Sigma Rules Are Not Enough: Why Detection Validation Matters
Why detection logic must be supported by telemetry requirements, parsing checks, false-positive analysis, validation evidence, and analyst handoff notes.
A Sigma rule is a portable description of detection logic. It is not proof that the required events are collected, parsed, searchable, and useful to an analyst. Validation connects the written rule to the environment in which it must work.
A rule is not a working detection
A syntactically valid rule can still produce no alert, too many alerts, or an alert with insufficient context. A working detection needs a defined behavior, reliable telemetry, correct field mappings, tested logic, and a clear investigation path.
Required telemetry and fields
Before testing logic, I write down the data contract: source, event category, required fields, expected values, and time constraints. Process detections may depend on image, command line, parent process, user, host, and timestamp fields. Missing one of these can weaken both matching and triage.
Parsing and ingestion problems
When validation fails, changing the rule first is often premature. I check whether the endpoint created the event, the collector received it, the pipeline parsed it, and the SIEM indexed it under the expected field names. This isolates a detection failure from a telemetry failure.
False positives and analyst workload
Validation also asks what legitimate activity matches. I record expected administrative tools, common automation, and environment-specific exceptions. Tuning should preserve the behavior of interest while giving analysts enough context to make a decision.
Validation evidence
A useful validation record includes the test objective, expected evidence, observed events, alert result, latency, gaps, and reviewer notes. Failed tests remain evidence: they show which assumption was incorrect and what must change.
SignalProof as a learning platform
SignalProof is my personal project for exploring contract-driven telemetry and detection validation. It uses YAML-defined expectations to organize checks, evidence, and regression comparisons. It is a learning platform, not a claim of production deployment.
Conclusion
Detection quality is a chain. Sigma can express the logic, but telemetry readiness, parsing, testing, and analyst handoff determine whether that logic becomes dependable SOC work.