Table Of Content
The Demo Test: Six Scenarios to Make Any AI Security Vendor Run Live
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November 10, 2026
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Feature lists look alike. Live scenarios do not.
AI security is a crowded category. Every vendor claims discovery, agent visibility and data protection. On 29 October, Cyberhaven published a buyer’s guide with six evaluation criteria, and its core advice is sound for any buyer: ask the vendor to demonstrate, not describe. Here is our adaptation, written from an endpoint point of view, that you can use with any vendor, including us.
1. Find what IT never approved
Ask the vendor to enumerate coding assistants, local agents and MCP servers installed on a developer laptop that nobody sanctioned. If the answer depends on browser or SaaS telemetry alone, the inventory has a blind spot. Cyberhaven Labs reports endpoint agentic app users grew 43.2% between January and June 2026 (their figure, not independently verified).
2. Replay an agent run
Have the vendor show what an agent did: files read, commands run, tools called, network destinations. Then ask for a scenario where one agent hands output to another. If you must stitch logs from several consoles, the audit trail is not really an audit trail.
3. Follow the data, not the file
Ask whether sensitive content keeps its classification after it is copied, transformed or pasted into a prompt. Enforcement that only knows data moved is weaker than enforcement that knows what the data is.
4. Look at the block message
Trigger a policy on sensitive data in a prompt and look at what the user sees. A generic block page pushes people to personal accounts. A good message names the risk, explains the policy and offers a way forward.
5. Cross the browser and the desktop
Start a workflow in a browser AI tool and continue it in a locally installed agent. Check that one policy and one classification layer cover both, and that personal and corporate accounts are told apart at runtime.
6. Correlate with the rest of data security
Ask whether an AI alert can be tied to a DLP or insider-risk event on the same data movement in one view, without manual correlation.
Add two questions of your own
- Cost to run: what is the agent's CPU footprint and typical time to deploy?
- Privacy: what does it store, for how long, and who can see it?
Where Kitecyber fits
Kitecyber runs on the endpoint, so device posture, operating system and process activity, user activity, network activity and data classification (80+ categories) share one context. The agent uses under 2% CPU and typical deployment takes around a day. We welcome these six scenarios in a live session.
Book a Kitecyber walkthrough at kitecyber.com and bring your own test cases.
