Demo library
Ten chapters, each one a problem Horizon Inc has and the part of Cloudflare One that solves it. Each stands alone, and the named paths below string them into a demo for a particular room.
Twenty-five minutes, a general audience: the whole of Cloudflare One.
Safe AI adoption: the walkthrough, or one of five single-control scripts.
A specific question: pick the chapter that answers it. Every chapter page says what it needs before you start, and which part of it is not built yet.
Paths
The whole of Cloudflare One
The overall demo: identity, retiring the VPN, data protection, AI, and the evidence afterwards - one company, one afternoon.
Replace the VPN
For a room that runs a VPN concentrator and is tired of it.
- 1. Identity and per-application access
- 2. VPN replacement for an internal app
- 3. Privileged SSH and database access
- 7. Device posture for sensitive applications Planned
Secure web gateway and data protection
For a room whose question is what leaves the building, and how.
- 5. DNS filtering for office networks Planned
- 6. DLP on uploads and on internal apps
- 4. Clientless isolated access for contractors Planned
AI security
For a room whose staff and agents are already using AI, sanctioned or not.
The full tour
Every chapter, in order. For a workshop rather than a meeting.
- 1. Identity and per-application access
- 2. VPN replacement for an internal app
- 3. Privileged SSH and database access
- 4. Clientless isolated access for contractors Planned
- 5. DNS filtering for office networks Planned
- 6. DLP on uploads and on internal apps
- 7. Device posture for sensitive applications Planned
- 8. Shadow AI, and the sanctioned path
- 9. Securing MCP and agent access
- 10. Evidence: logs, decrypted payloads, prompts
Safe AI adoption
Where this lab started, and still the deepest part of it: what happens when a company's AI agent can reach everything its user cannot. One continuous walkthrough, and five scripts that each prove one control.
The walkthrough: one person, one afternoon →
Eleven steps, alternating between the app and the agent. Alice reads a rumour about an acquisition and a restructure, looks for the truth in four applications that each correctly tell her nothing, and asks her assistant the same questions instead. It begins with her pasting a colleague's customer list into a public AI to tidy it up, and ends with "What is Project Ironwood?" being answered in a paragraph.
Five controls, one prompt each
In this order. Between them they cover every control in the AI stack rather than the same policy five times over. Each stands alone.
| Script | Cloudflare control | What it proves to the customer |
|---|---|---|
| Asking the company assistant to help commit a crime | AI Gateway Guardrails (violent and non-violent crime, weapons, self-harm) | Governs what your staff can make the company's AI do, consistently across every model - instead of relying on each vendor's own refusals. Blocked prompts cost no tokens. |
| Pasting a colleague's details into a chat | AI Gateway DLP (request and response) | Covers the everyday incident with no agent in it: someone pasting personal data into a chat. Same profiles as the MCP path, second choke point. |
| The CEO's home address | Gateway DLP on MCP traffic (Employee PII) | Your apps can be carefully built and still hand an agent a home address. One profile, applied at the network, stops it without touching the app. |
| The email nobody clicked | AI Gateway Guardrails (prompt injection) + Gateway DLP | Anyone who can send your staff an email or a calendar invite can leave instructions for their AI. Two independent layers stop it, neither relying on the model's judgement. |
| The application she cannot see at all | Cloudflare Access policy + MCP portal scoping | The cheapest control of all: the app and its tools are not yours to see. An agent cannot call a tool that was never listed for your identity. |
Before running any of them: an agent client set up as in
setup, signed in as everywhere; a fresh conversation for each
script; and each one run twice - protection off, then on - because nothing about the app, the data or the
prompt changes between the two halves, only what sits in the path. Chapters
8 and 9 frame the same material as
library chapters.
Chapters
7 of 10 can be run today. Planned chapters are listed so the shape of the library is visible; they have no script yet. See coverage for every capability and where it is shown.
| # | Chapter | Proves | Status |
|---|---|---|---|
| 1 | Who are our people, and what are they allowed to reach? | Access, FlareID (OIDC + SCIM), App Launcher, Access audit log | Ready |
| 2 | Retire the VPN concentrator. | Cloudflare Tunnel, Private network routes, Split tunnels, Internal DNS, Resolver policies, Access | Ready |
| 3 | Admins reach servers without standing credentials. | Access for Infrastructure, SSH certificate authority, SSH command logging, Access for SaaS (OIDC), Gateway network policies | Ready |
| 4 | Contractors get access; they do not get a laptop. | Clientless Browser Isolation, One-time PIN, Access | Planned |
| 5 | Filter the internet for an office with no appliance. | Gateway DNS policies, DNS locations, Block page | Planned |
| 6 | Stop company data leaving through the browser. | Gateway HTTP policies, DLP profiles, Application types, Payload logging, Antivirus | Ready |
| 7 | Only healthy, managed devices reach production. | Device posture, Device profiles, DEX | Planned |
| 8 | Our staff are pasting customer data into ChatGPT. | Application Library, Gateway HTTP policies, Browser Isolation, AI prompt logging | Ready |
| 9 | Our AI agent reaches what the person driving it cannot. | MCP server portal, Access, Gateway DLP on MCP traffic, AI Gateway | Ready |
| 10 | Show the auditor what happened. | Access audit log, Gateway activity log, Payload and prompt logs | Partial |
Before any of them
- Setup: the Cloudflare One client, the agent client, and the one-time logins.
- Lab only means the chapter reaches the private estate on the lab VM - a server with a private address, which a laptop deploy does not have.
- Sign in as the person each chapter names. Most use Alice Watson, a content strategist with no special access anywhere.