AI Zero Trust extends established Zero Trust principles to AI users, agents, models, gateways, and connected services. It requires each prompt, model response, identity, and agent request to be checked against policy before crossing a trust boundary. This matters because AI-driven attacks can move quickly: the source notes that the time between vulnerability exposure and business disruption has shrunk from weeks to minutes.

The source identifies two strategic control points: an AI Network Firewall and a Secure LLM Gateway. The AI Network Firewall adds AI-aware inspection and segmentation, including prompt-injection detection and policy enforcement between users, agents, models, and enterprise systems. The Secure LLM Gateway governs how AI applications and agents connect to internal and external models, with secure routing, prompt defense, and content and policy inspection.

AI Zero Trust builds on three established principles: never trust, always verify; least privilege; and assume breach. In practice, inspect AI interactions before they proceed, give each agent and tool only the permissions it needs, and design boundaries to contain compromise. The source also emphasizes continuous validation and fast recovery. For example, agents should not inherit a user's broad permissions, and their API calls and tool use should be scoped and authorized at runtime.