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.
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 …
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 …
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 …
Proving compliance is a necessity, but in a world of tightening regulations, the path to compliance is currently paved with spreadsheets, screenshots, and manual attestations. We call this the “Audit Tax”, the millions of dollars and thousands of people hours spent not just integrating security, but on proving you are handling security. With the advent …
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 …
In an AI-native world where Model Context Protocol (MCP) is the universal standard for AI connectivity, the security and governance stakes have never been higher. AI’s ability to take autonomous action through MCPs means that a single breach of an MCP server can grant attackers control over mission-critical enterprise systems, putting enterprises in an immediate …
In 2026, software development speed is an AI-solved problem. Yet, as AI-generated code volumes surge, organizations face a new kind of risk visibility gap. Developers are increasingly copying third-party snippets into their codebases—from both AI prompts and open-source software components—creating large security and compliance blind spots that lead to significant risks. While proven software composition …
Imagine your lead Software Engineer walks into your office and says, “Good news! I just deployed that critical update to production. I wrote the code on my personal laptop, didn’t run it through CI/CD, skipped the security scan, and just copied the files directly to the server with a USB drive.” You would fire them. …
Listen to a NotebookLM podcast version of the blog: When Anthropic announced Claude Code’s new security scanning capabilities, following the announcement of OpenAI’s Aardvark, it marked an important moment for the industry. For the first time, expert-level security review is becoming embedded directly into the act of writing code. Subtle, context-dependent vulnerabilities can now …
In today’s AI rush, I’ve seen even the most disciplined organizations find it almost impossible to apply the hard-won lessons of DevOps and DevSecOps onto AI adoption. These organizations often feel forced to choose between moving fast and staying in control. As a result, they develop a “wait and see” approach to AI usage and …
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