Managing False Positives and Scan Results

False positives represent one of the biggest challenges in SAST adoption. When tools flag secure code as vulnerable, developers lose trust and may ignore legitimate findings. Modern SAST tools employ various strategies to reduce false positives, including machine learning models trained on labeled datasets and contextual analysis that considers surrounding code patterns.

Effective false positive management requires establishing clear processes for reviewing and dispositioning findings. Create a security champions program where experienced developers help triage SAST results, distinguishing real vulnerabilities from false positives. Document disposition decisions to train both developers and tools – many SAST platforms can learn from user feedback to improve future accuracy.

Result prioritization helps teams focus on the most critical vulnerabilities first. Consider factors beyond just severity ratings, including exploitability, data sensitivity, and business impact. Vulnerabilities in authentication code or payment processing deserve immediate attention, while issues in internal tools might be addressed during regular maintenance. Some teams use risk scoring algorithms that combine multiple factors to automatically prioritize findings.