The Demo Test: Six Scenarios to Make Any AI Security Vendor Run Live
Table Of Content 1. Find what IT never approved 2. Replay an agent run Add two questions of your own
Approved Isn’t Static: Catching When a Sanctioned AI Tool Changes Behavior
Table Of Content Why a point-in-time approval decays The correlation problem What the endpoint contributes From drift to action Where
Localhost Is Not a Security Boundary: The MCP Servers Running on Your Developers’ Laptops
Table Of Content What the advisories describe Why this is an endpoint problem A practical checklist Where Kitecyber fits Related
Detection Alone Falls Short: What Regulators Now Expect for AI, and Why It Lands on the Endpoint
Table Of Content What “preventative” and “continuous” demand in practice Why the endpoint is the natural enforcement point A practical
One Integration per Agent Doesn’t Scale: Securing Every AI Agent on the Device
Table Of Content The arithmetic of a real laptop What stays constant across agents A layered way to think about
Small Bugs, Long Chains: What Agent Kill-Chain Research Says About Endpoint Controls
Table Of Content What this means for a fleet of laptops Checks the endpoint can make Where Kitecyber fits Related
Agents in a Box: What Windows Execution Containers Solve, and What Still Needs Endpoint Telemetry
Table Of Content What containment does well Where the gaps remain A practical way to layer the controls Where Kitecyber
From Alerts to Baselines: Dynamic Risk Scoring for AI Tools, Device by Device
Table Of Content Two populations of agents What a runtime boundary cannot see A practical split of responsibilities Keep it
The Agent Runtime You Don’t Control
Table Of Content Two populations of agents What a runtime boundary cannot see A practical split of responsibilities Keep it
260 AI Services in One Organization: Turning Shadow AI Discovery Into Policy
Table Of Content Discovery is the start of the work Tier the list by decision, not by vendor What the
