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The Year the Vulnerability Backlog Changed Shape

As we enter the AI era, few among us have found themselves so entrenched in the throes of the shifting landscape as CISOs. With the threat landscape shifting, they are up against increasing rates of vulnerabilities and exploits alongside rapidly scaling demands for ever more secure environments. As CISOs, with our role as the protectors …

The New Rules of Patching: When a Fix Becomes a Blueprint for Attackers

TL;DR: Frontier AI has collapsed the vulnerability exploit window from weeks to mere hours. Attackers now use advanced AI models to reverse-engineer published fixes and generate working exploits faster than human security teams can deploy updates. In the AI era, publishing a patch creates a blueprint for attackers. Surviving this threat requires vendors to tier sensitive …

Agent Guard: Control AI Assets Before They Become Shadow AI

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 — …

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Secure AI Workflows: The Identity and Access Management (IAM) Checklist

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 …

Accelerating AI Agent Development on Google Cloud with JFrog MCP Registry

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 …

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Building a Governed AI Model Supply Chain: Integrating AWS SageMaker and the JFrog Platform

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 …

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You Can’t Trust What You Can’t Trace

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 …