Managing False Positives and Noise

False positive management separates successful security automation from abandoned initiatives. Establish clear processes for reviewing and dispositioning findings. Initial triage might use automated rules based on code patterns, file locations, or historical false positive data. Human review focuses on unclear cases, with decisions documented for future automation. Machine learning models can learn from triage decisions, continuously improving accuracy.

Baseline establishment prevents existing issues from overwhelming new initiatives. Before enforcing security gates, scan applications to understand current vulnerability state. Create remediation plans for existing issues while preventing new vulnerabilities. Some teams implement "grandfather" policies where existing issues generate warnings while new issues block deployments. This approach maintains momentum while systematically improving security posture.

Suppression mechanisms must balance security with practicality. Allow developers to suppress findings with appropriate justification and approval. Time-bound suppressions ensure periodic review of accepted risks. Some organizations require executive approval for suppressing high-severity findings. Track suppression metrics to identify patterns requiring process or training improvements. Excessive suppressions might indicate overly strict policies or tool misconfiguration.