At its annual consumer convention, swampUp, the DevOps firm JFrog introduced new options and integrations with corporations like GitHub and NVIDIA to allow builders to enhance their DevSecOps capabilities and produce LLMs to manufacturing rapidly and safely.
JFrog Runtime is a brand new safety answer that enables builders to find vulnerabilities in runtime environments. It displays Kubernetes clusters in actual time to establish, prioritize, and remediate safety incidents primarily based on their danger.
It supplies builders with a technique to trace and handle packages, manage repositories by surroundings varieties, and activate JFrog Xray insurance policies. Different advantages embody centralized incident consciousness, complete analytics for workloads and containers, and steady monitoring of post-deployment threats like malware or privilege escalation.
“By empowering DevOps, Information Scientists, and Platform engineers with an built-in answer that spans from safe mannequin scanning and curation on the left to JFrog Runtime on the suitable, organizations can considerably improve the supply of trusted software program at scale,” mentioned Asaf Karas, CTO of JFrog Safety.
Subsequent, the corporate introduced an growth to its partnership with GitHub. New integrations will present builders with higher visibility into venture standing and safety posture, permitting them to handle potential points extra quickly.
JFrog clients now get entry to GitHub’s Copilot chat extension, which might help them choose software program packages which have already been up to date, accepted by the group, and protected to be used.
It additionally supplies a unified view of safety scan outcomes from GitHub Superior Safety and JFrog Superior Safety, a job abstract web page that reveals the well being and safety standing of GitHub Actions Workflows, and dynamic venture mapping and authentication.
Lastly, the corporate introduced a partnership with NVIDIA, integrating NVIDIA NIM microservices with the JFrog Platform and JFrog Artifactory mannequin registry.
In line with JFrog, this integration will “mix GPU-optimized, pre-approved AI fashions with centralized DevSecOps processes in an end-to-end software program provide chain workflow.” The tip end result might be that builders can deliver LLMs to manufacturing rapidly whereas additionally sustaining transparency, traceability, and belief.
Advantages embody unified administration of NIM containers alongside different belongings, steady scanning, accelerated computing via NVIDIA’s infrastructure, and versatile deployment choices with JFrog Artifactory.
“As enterprises scale their generative AI deployments, a central repository might help them quickly choose and deploy fashions which can be accepted for improvement,” mentioned Pat Lee, vice chairman of enterprise strategic partnerships at NVIDIA. “The combination of NVIDIA NIM microservices into the JFrog Platform might help builders rapidly get absolutely compliant, performance-optimized fashions rapidly working in manufacturing.”
