At its annual person 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 shortly and safely.
JFrog Runtime is a brand new safety resolution that enables builders to find vulnerabilities in runtime environments. It displays Kubernetes clusters in actual time to determine, prioritize, and remediate safety incidents based mostly on their threat.
It supplies builders with a technique to trace and handle packages, set up repositories by surroundings sorts, 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 resolution that spans from safe mannequin scanning and curation on the left to JFrog Runtime on the correct, organizations can considerably improve the supply of trusted software program at scale,” stated Asaf Karas, CTO of JFrog Safety.
Subsequent, the corporate introduced an enlargement to its partnership with GitHub. New integrations will present builders with higher visibility into venture standing and safety posture, permitting them to deal with potential points extra quickly.
JFrog prospects now get entry to GitHub’s Copilot chat extension, which might help them choose software program packages which have already been up to date, authorised 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 exhibits 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 keeping 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 outcome will probably be that builders can carry LLMs to manufacturing shortly whereas additionally sustaining transparency, traceability, and belief.
Advantages embody unified administration of NIM containers alongside different belongings, steady scanning, accelerated computing by 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 might be authorised for improvement,” stated Pat Lee, vice chairman of enterprise strategic partnerships at NVIDIA. “The combination of NVIDIA NIM microservices into the JFrog Platform might help builders shortly get absolutely compliant, performance-optimized fashions shortly working in manufacturing.”