导航|地址|刷新
Monitor AI usage at your organization

Skip to contentDocumentation Index
Fetch the complete documentation index at: https://developers.cloudflare.com/learning-paths/llms.txt
Use this file to discover all available pages before exploring further.



DocsDirectoryAPISDKsChangelog

Search

Log in






Holistic AI security with Cloudflare One (Learning Paths)

/





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 ↗

Learning Paths llms-full.txt ↗

Cloudflare Docs llms.txt ↗

Cloudflare Docs llms-full.txt ↗













Home

/Learning Paths

/Holistic Ai Security

/Monitor AI usage at your organization



Monitor AI usage at your organization


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




Monitoring AI usage is one of the first steps in assessing holistic risk. Cloudflare provides a number of ways to understand both sanctioned and unsanctioned use of AI and chat applications within your Zero Trust deployment. Reviewing your detected usage should give you a sense of which AI apps are used in your organization as well as what type of policies you need to create based on what should be supported versus restricted.

After you begin to understand your organization's AI usage, you should develop a balanced perspective on what constitutes sanctioned use of a tool. A good practice is to review applications in phases and then apply security policies based on those phases. For example, you can apply different security policies to applications that are considered approved, unapproved, unreviewed, or in review until they pass your corporate security standards and data privacy reviews.

This section will detail options for assessing and understanding that risk by using inline (client) and out-of-band (API CASB) based methods.


PreviousDefine your company's AI risk tolerance and strategyNextReview inline AI use


Was this helpful?

YesNo

Edit pageReport issue




On this page

Overview


Edit pageReport issue














Getting started
PlansContact salesPartnersFind a partnerStartupsUnder attack?Domain name search


Company
AboutCareersInvestorsPressPress kitGlobal network




Public interest
Project GalileoAthenian ProjectCloudflare for CampaignsProject FairshotImpact/ESG


Compliance
Compliance resourcesTrust HubData ProtectionResponsible AITransparency reportReport abuse




Resources
App innovation reportCloudflare RadarCase studiesStatusSupportEventsBlog


Developers
DocumentationLearning centerCommunity




Solutions
SSE and SASE platformCloudflare AI CloudAI SecurityFrontend Development PlatformMulti-Tenant Platform DevelopmentWeb Security Platform


Start BuildingLog In












© 2026 Cloudflare, Inc.

Privacy policy|Report security issues|Terms of use|Trademark
|
Your privacy choices








Docs




------
导航页 打开网址