PPhying中文
Curated security research, vulnerabilities, advisories and tools for practitioners.

AI for everyone, with confidence

metabase.com | blog | #ai-security | #mcp | #metabase | #ai-analytics | #governance | #metabot | #data-studio
📄 Raw .md⬇️ Download .md🔗 Original ↗

Summary

A roundup of Metabase's 2026 AI releases — Metabot, MCP server, Data Studio, AI SQL and Python generation — framed around trustworthy answers, usage controls and availability in every edition.
Published
Collected

Related coverage

blog · metabase.com
'Wait, you don't have a semantic layer yet?' AI analytics readiness pulse
We asked 73 data people about AI Analytics: 59% are already using AI with their data, 44% want it all self-hosted, and 31% of poll voters don’t know what a semantic layer is. A small, unscientific snapshot from AI Analytics Week.
blog · metabase.com
The bottleneck moved: how to build a semantic layer for AI Analytics
AI removed the querying bottleneck and created a verification one. Here's the semantic layer that makes AI-generated answers traceable, and how to build it.
blog · metabase.com
What is a semantic layer? A definition for the AI analytics era
What a semantic layer is, what's in one, how AI changed how they're built, and whether you still need one for accurate AI analytics.
blog · metabase.com
Join us for AI Analytics Week
This coverage gives security teams current context for monitoring, validation, and remediation.
blog · about.gitlab.com
New MCP tools help platform teams scale automation safely
GitLab 19.4 expands the GitLab MCP server tool set so agents can run pipelines, open merge requests, and triage vulnerabilities, while writes, merges, and deletes still wait for a human reviewer.
blog · jfrog.com
The Evolution of JFrog AI Catalog: Your AI Control Plane for Agentic Development
JFrog AI Catalog evolves into an AI control plane for the Agentic Development Lifecycle: models, skills, plugins, and MCP servers become native artifacts, so agents consume only approved AI assets.
Back