As development teams rapidly integrate generative AI, security teams encounter new challenges in safeguarding the software development life cycle, particularly as legacy scanning tools struggle to keep pace with the increasing speed and scale of code changes. Datadog Code Security addresses these challenges by utilizing AI-driven automation to combine static and runtime analysis, effectively scanning repositories for vulnerabilities in first-party code, open-source dependencies, and infrastructure-as-code misconfigurations. It excels in detecting hidden code vulnerabilities and validating findings by filtering out false positives to reduce alert fatigue and improve remediation time. By employing large language models (LLMs), Code Security evaluates code behavior in context, identifying risky code changes in pull requests that traditional static analyzers might miss. The system allows for the prioritization of high-risk findings, enabling teams to focus on the most actionable issues by providing transparency in vulnerability classifications. Moreover, Code Security facilitates batch remediation by generating proposed code patches through AI collaboration, allowing developers to efficiently resolve vulnerabilities without disrupting their workflow. This modern approach integrates AI-native analysis with a focus on developer experience, offering a comprehensive solution to secure applications in today's fast-paced development environment.