Operational Efficiency Metrics

False positive rate significantly impacts developer trust and operational efficiency. Track what percentage of findings developers mark as false positives versus confirmed vulnerabilities. Rates above 20% indicate need for tool tuning or policy refinement. Analyze false positive patterns—specific vulnerability types, component categories, or detection methods—to guide improvements. Declining false positive rates demonstrate program maturation.

Automation effectiveness metrics quantify how much manual effort SCA eliminates. Track the percentage of vulnerabilities fixed through automated pull requests versus manual remediation. Measure time saved through automated license compliance reporting versus manual review. Calculate the ratio of automated to manual security assessments. High automation rates indicate efficient use of security resources.

Policy exception rate reveals whether security policies align with business reality. Track how often teams request exceptions to vulnerability or license policies. High exception rates suggest overly restrictive policies, while very low rates might indicate policies aren't being enforced. Analyze exception reasons to identify policy adjustments. The goal is policies that provide security value while enabling business operations.