3.5x more true positives: How we benchmark AI-powered detection

The text discusses the application of AI-powered detection tools, specifically Semgrep Multimodal, in identifying code vulnerabilities compared to other models such as Opus 4.8 and GPT 5.5. It highlights that while AI models can be effective at identifying some vulnerabilities, they often miss a significant portion of the code due to a lack of comprehensive coverage. Semgrep Multimodal, however, employs deterministic program analysis to ensure thorough examination of codebases, leading to significantly higher recall rates and reduced cost per true positive compared to other approaches. The text emphasizes the importance of both the AI model and the surrounding architecture or scaffolding, suggesting that combining improved models with structured workflows yields better overall performance. It concludes that effective risk reduction in AI-powered security tools depends on the balance between model capabilities and the orchestrating framework around them.

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