Optimizing Scanner Accuracy

False positive reduction requires continuous tuning based on organizational patterns. Maintain false positive tracking systems recording disposition decisions. Analyze patterns identifying common false positive sources. Develop custom rules or suppressions addressing organization-specific code patterns. Share false positive intelligence across teams preventing duplicate triage efforts.

True positive validation ensures scanners catch real vulnerabilities. Regularly test scanners with known vulnerable code samples. Participate in scanner benchmarking initiatives comparing detection rates. Monitor vulnerability escape rates to production identifying scanner blind spots. Correlate production incidents with scanner findings identifying missed detections.

Rule tuning balances security coverage with practical constraints. Start with vendor-recommended rule sets then customize based on experience. Document rule modification rationale for future reference. Test rule changes against representative code samples ensuring intended effects. Monitor metric changes after rule modifications validating improvements.

Machine learning model optimization improves detection accuracy over time. Provide feedback on scanner findings training ML models. Ensure diverse training data representing your code patterns. Monitor model drift as code patterns evolve. Retrain models periodically incorporating new vulnerability patterns and coding practices.