Datadog has introduced a new false positive filtering feature for its Static Code Analysis (SAST), aimed at improving the efficiency of security vulnerability detection by leveraging Bits AI to differentiate between true and false positives. This enhancement helps reduce noise and distractions by classifying vulnerabilities and providing context, allowing development and security teams to focus on genuine threats. The AI-driven feature integrates into Datadog's SAST platform, enabling seamless triage of findings and facilitating transparency through confidence badges and detailed reasoning for each assessment, which users can validate and provide feedback on. By analyzing vulnerabilities in the context of their broader code structure, Bits AI enhances accuracy beyond traditional static analyzers, which often generate false positives due to their risk-averse design. The implementation of this feature is part of Datadog's ongoing investment in AI innovation for improving code security, and it is available to Datadog Code Security customers, with a 14-day free trial for new users.