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Holistic AI security with Cloudflare One (Learning Paths)

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Concepts


Overview

What is Shadow IT?

What is Shadow AI?

What is Model Context Protocol (MCP)?






Get started


Overview

Additional setup for AI security solutions in Zero Trust

Define your company's AI risk tolerance and strategy






Monitor AI usage at your organization


Overview

Review inline AI use

Review out-of-band AI use

Monitor prompts and responses






Build security policies for general AI use


Overview

Set policy based on approval status





Overview


Appendix


Use Cloudflare Workers to create custom user coaching pages






Agent resources


Agent setup ↗

Cloudflare Skills ↗

Code Mode MCP Server ↗

Domain-specific MCP Servers ↗MCP

Learning Paths llms.txt ↗

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Concepts


Last updated Apr 23, 2026|Copy as Markdown|View as Markdown|Agent setup

OverviewObjectives




The goal of this learning path is to provide Cloudflare One users with the strategy and tools to securely adopt generative AI within their organizations. This guide will help address new security challenges and mitigate risks like shadow AI and data loss.


Objectives


Determine risk tolerance: Identify areas of concern and risk tolerance for AI use to establish a baseline for your organization's AI security strategy.

Monitor AI usage: Utilize Cloudflare One's tools, such as the Shadow IT dashboard and API CASB integrations, to gain visibility into both sanctioned and unsanctioned AI application usage.

Build security policies: Create granular security policies using Cloudflare Gateway to control AI usage, prevent data loss with DLP, and manage user behavior through actions like blocking or redirecting.

Secure sanctioned models: Apply Zero Trust principles to sanctioned AI models and internal services like Model Context Protocol (MCP) servers to ensure secure access and protect sensitive data from being exposed.


NextWhat is Shadow IT?


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