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.
A developer on your team just told Claude Code to connect to a new MCP server, the protocol coding agents use to reach organizational tools and data. Nobody in security reviewed it. Nobody in security even knows it happened. For two-thirds of enterprises, the primary obstacle to scaling agentic development isn’t budget or headcount — …
Just last week, OpenAI and Hugging Face jointly disclosed what may be the first incident of its kind: during an internal evaluation of frontier cyber capabilities, OpenAI’s models, running deliberately without production safeguards in an isolated research environment, autonomously discovered and employed chained vulnerabilities to escape its sandbox, reach the open internet, and extract evaluation …
AI agents and LLMs are already building, analyzing, and deploying code across your software development lifecycle. As software supply chains become increasingly AI-driven, proactive security and access controls are your only path to success. To effectively govern authentication and permissions without sacrificing development speed, you must update your access management strategies. By securing the AI …
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 …
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 …
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 …
The software supply chain is no longer just about shipping code, it is about managing intelligence and risk. As DevOps, DevSecOps, DevGovOps and AI/ML practices converge into a single AI-driven and increasingly agentic delivery pipeline, the demands on development and security teams have reached a new level. The platform that once managed packages and artifacts …
Let’s talk about agent skills. As the AI agent ecosystem matures, we’re seeing a major shift in how users equip agents to run automated workflows. While robust protocols such as MCP exist to handle complex system integrations and authentication, skills have emerged as the go-to, low-friction way to shape an agent’s day-to-day behavior. Skills are …
Sponsored by JFrog ~ Development teams are shipping faster than ever. Generative AI coding assistants, early agentic workflows, and increasingly modular architectures have compressed the distance between concept and deployment. AI-enabled innovation has become an executive mandate, and teams are expected to deliver at speed without sacrificing security or compliance. At the same time, modern …
The race to adopt AI agents has created a massive, unmonitored blind spot in the enterprise software supply chain. At the heart of this revolution is the Model Context Protocol (MCP) – an open connectivity standard designed to move AI models (LLMs) out of their passive “chat box” and give them direct active access to …
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