Most AI security guards the model. We guard the wire.
AI security tooling clusters into four architectures. Each watches one door. AI traffic uses every door: browser tabs, desktop apps, command lines, and autonomous agents that call APIs directly. The one place all of it passes through is the device's own network stack. That is where NeverTrust.ai sits.
Browser extensions and endpoint sensors
See what happens inside the browser. Everything outside it, desktop apps, CLIs, autonomous agents, needs its own per-app sensor and its own integration work.
Model-security platforms
Scan, red-team, and posture-manage the models a company builds. Valuable work, but it does not watch the AI tools employees already use every day.
Cloud proxies and SSE DLP
Reroute traffic through a cloud inspection point. Sensitive content leaves the device before it is scanned, and anything that bypasses the tunnel is invisible.
API gateways and SDKs
Guard the applications a team deliberately routes through them. Shadow AI, by definition, is never routed through them.
on the device, inside the session
Every app, one agent
The agent intercepts outbound HTTPS at the network layer. Browser, desktop app, CLI, or agent framework: if it talks to an AI service, it is inspected. New tools are covered the day they appear.
Not a cloud proxy
Inspection happens on the device, inside the TLS session. Full response bodies never leave the device; only classification scores and metadata reach the portal. If our cloud is unreachable, traffic still flows.
Nothing to integrate
No SDK, no code changes, no traffic rerouting, no per-app sensors. An embedded ML model classifies prompt injection and agentic behaviour in milliseconds, offline, with policy rules enforced inline.
growth in a year of enterprise data flowing into genAI apps
netskope cloud & threat report 2025
of breached organisations link the breach to shadow AI
ibm cost of a data breach 2025
of organisations reporting AI-related breaches lacked AI access controls
ibm cost of a data breach 2025