Remediate transitive vulnerabilities faster with Datadog Software Composition Analysis

Security teams face the complex task of identifying and remediating vulnerabilities in applications due to the intricate web of dependencies and transitive dependencies within frameworks, SDKs, and utilities. Datadog Software Composition Analysis (SCA), part of Datadog Code Security, assists teams in addressing these challenges by providing visibility into the dependency chain, which helps identify the root library introducing vulnerable dependencies and recommends safe upgrades. The process involves detecting vulnerabilities such as Log4Shell, connecting them to their root libraries, and offering guidance for upgrades that ensure minimal disruption to the build. This comprehensive approach not only highlights the necessary changes but also considers the wider impact on the codebase, facilitating a more efficient and secure remediation process. Datadog's internal research indicates that around 70% of vulnerabilities stem from transitive dependencies, emphasizing the importance of tools like Datadog SCA in closing the gap between vulnerability detection and effective resolution.