Traditional scanners surface thousands of findings (CVEs, code vulnerabilities, exposed secrets, misconfigurations) but miss the context to tell you which actually matter, who should fix them, or the fastest path to remediation. Disconnected tools and manual handoffs lead to alert fatigue, unclear ownership, and slow fixes.

This session shows that end-to-end remediation is possible when a single platform unifies visibility from development to production. We'll show how Datadog proves what's actually reachable and exploitable across code and cloud, applies risk-based prioritization, identifies bulk remediation actions, assigns them to the right team, and generates AI-driven, context-aware pull requests to resolve issues at scale.