Detect attacks on applications and APIs so security operations teams can respond before exploits occur.
X
of code is AI-generated 1
of codebases had open source software vulnerabilities 2
of apps had security flaws 3
Relying on traditional security methods involving slow, infrequent scans and manual tasks.
Struggling to keep pace with the high velocity of modern CI/CD pipelines and rapid deployment cycles.
Accumulating unseen security gaps and vulnerabilities with every new code deployment.
Challenged with the increasing volume of AI-generated code that overwhelms traditional, point-in-time testing methods such as SAST and DAST.
Missing the critical runtime context needed to find real vulnerabilities in a dynamic, AI-powered development environment.
Deploying applications with hidden weaknesses that are only discoverable through runtime context, leaving them vulnerable in production.
Overwhelmed by a high volume of false positives generated by existing security tools, which diverts valuable resources and delays the remediation of genuine threats.
Struggling with the limitations of penetration testing and other point-in-time security assessments that are not aligned with modern development.
Facing significant bottlenecks because of delayed security feedback, making remediation more costly and time-consuming.
Detect attacks on applications and APIs so security operations teams can respond before exploits occur.
Prioritize and address risks with faster application and API vulnerability detection and fewer false positives.
Managed runtime security powered by the people who built it.
