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 …
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 …
When we first introduced the JFrog AI Catalog, it was our mission to provide the industry with a single system of record for governing the complex landscape of internal, open-source, and external commercial AI models. This foundational step was critical for enterprises to move from uncontrolled innovation to delivering AI with trust and confidence. However, …
Today’s software organizations can’t make tradeoffs between speed and trust – you need both to succeed. But juggling them is tough. Moving too fast can lead to security vulnerabilities and compliance issues, while moving too slow means your competitors beat you to market. This tension creates friction that slows down every release, a problem that …
The rapid pace of AI innovation is driving new possibilities for every organization. Yet, for many, the journey from inception to reliable, production-ready AI applications is riddled with hidden challenges: proliferation of models, security blind spots, and a desperate need for consistent governance. You want to harness the power of AI, but not at the …
As software supply chains get more complex, your GRC initiatives become more critical. You need a deeper level of assurance that your software has gone through required processes as it moves toward release. The stakes are only getting higher; new software and AI regulations are gaining ground, and businesses that aren’t able to prove compliance …
Data-driven decisions are critical. And to support high-stakes decision-making – from fraud detection in credit card transactions to demand forecasting in retail – organizations are increasingly relying on complex models. According to McKinsey, 78% of organizations report using AI in at least one business function, highlighting just how embedded AI and ML models have become …
Attestations, or as we like to call them, evidence, are a critical piece to proving software supply chain integrity and security. However, without the right tools and processes, reviewing and verifying attestations can be time-consuming. At JFrog, we’re deeply committed to empowering developers, DevOps, and Security teams to make these complex workstreams as simple as …
