Your agents are only as trustworthy as what they consume, build, and ship. JFrog governs every AI model, agent skill, MCP server, AI-generated code, and assembled artifact in a single source of truth.
Secure your entire agentic software supply chain so you can ship trusted software at your new speed.
Developers building agentic AI on Google Cloud have powerful infrastructure at their fingertips: Gemini 3 for reasoning, Google’s Agent Development Kit (ADK) for orchestration, and a rapidly expanding ecosystem of Model Context Protocol (MCP) servers that connect agents to data and tools. So why are so many teams still waiting weeks to ship their first …
In the modern software factory, 99.9% uptime is no longer the gold standard. A standard 99.9% SLA translates to approximately 43 minutes of unexpected downtime per month. While industry data shows that a single minute of downtime costs an average of $9,000, for large global enterprises, that figure can easily be 5x higher. At tens …
Amazon SageMaker accelerates the process of training and deploying machine learning models. However, as AI adoption scales from individual experiments to enterprise-wide production, the focus of leading Fortune 500 software development operations and security teams must shift from pure velocity to governance. The question is no longer just “Can we ship this model?” but “How …
Agents are writing code, suggesting dependencies, and reviewing PRs, without any knowledge about your trusted package sources, security posture, or governance policies. When agents operate without supply chain context, they introduce risk, create rework, and weaken the guardrails DevSecOps teams rely on to ship with confidence. JFrog is changing that. Today, we’re launching an official …
For many engineering and security teams, NIST SP 800-218 (Secure Software Development Framework, or SSDF) compliance feels like a hurdle that is too difficult to overcome. To meet these and other emerging regulations and be effective in today’s DevSecOps environment, organizations are moving toward codifying these standards into machine-readable rules, also known as Policy as …
Picture this: Your security team finishes an AI vendor evaluation. The offering looks ironclad, with content filtering, output guardrails, and a stellar red-teaming report. Everyone leaves the meeting satisfied, and another governance box is checked. Six months later, a production incident hits. An AI agent, powered by a model your team “vetted,” starts executing unauthorized …
In celebration of International Women’s Month and the 2026 theme #GiveToGain, JFrog hosted a virtual fireside chat on March 19, 2026: Women in DevSecOps: Leveraging AI in the Software Delivery Lifecycle. Moderated by Shubha Gururaja Rao, Director of Solution Engineering at JFrog, the panel brought together two trailblazing technical leaders — Christine Tran, Head of …
Azure Machine Learning (AzureML) is a powerhouse for model experimentation and high-scale compute. However, for most organizations, the challenge isn’t building models; it’s the complex journey from a notebook to a secure, governed, and production-ready application. When models and dependencies reside in unmanaged silos, you lose the traceability required for production. This fragmentation creates Shadow …
Note: This blog was originally published in July 2024 and updated on an annual basis. It was most recently updated in April 2026. Regulatory compliance is a common and critical part of today’s rapidly evolving financial services landscape. One new regulation that EU financial institutions must adhere to is the Digital Operational Resilience Act (DORA), …
In a large-scale DevOps environment, small discrepancies lead to massive headaches. You’ve likely experienced it: a script runs perfectly on a developer’s laptop but fails in the production pipeline. You spend hours hunting for the cause, only to discover a mismatch in CLI versions. At JFrog, we know the JFrog CLI is vital to your …
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