How pairing SAST with AI dramatically reduces false positives in code security - InfoWorld

A novel hybrid framework combining Static Application Security Testing (SAST) with a fine-tuned Large Language Model (LLM) dramatically improves vulnerability detection accuracy. This approach reduces false positives by 91% and increases precision to 89.5%, leading to significant efficiencies in security analysis and developer remediation workflows.