NeuralTrust Documentation - AI Agent Security
Protect what your agents access and execute: models, tools, MCP servers, skills, and data, from one runtime layer.
This site is the API reference and setup guide for NeuralTrust. TrustGate is the gateway in front of models, tools, and MCP servers; TrustGuard evaluates that traffic at runtime; TrustTest runs adversarial probes against the same stack.
Start from an agent or provider below, or open a product guide when you already know which primitive you need.
Secure the agents you already run
Three primitives, one control plane
Use one product or all three. They share organization settings, not a single request path.
Platform
Users, SSO, and how you deploy the control and data planes.
FAQ
What is an AI agent gateway?
An AI agent gateway is a control plane that sits between AI agents and the models, tools, and APIs they call. It routes requests, enforces access policies, applies rate limits, and logs every interaction, giving teams governance and observability across all agent traffic from a single layer without modifying the agents themselves.
What is runtime security for AI agents?
Runtime security for AI agents means inspecting and enforcing policies on every request and response an agent makes while it is running in production. It detects threats like prompt injection, data leakage, and jailbreak attempts in real time, blocking policy violations before they reach the model or tool, and logging each event for audit.
What is AI red teaming?
AI red teaming is the practice of running adversarial attacks against an AI model, agent, or pipeline to find security and safety vulnerabilities before they are exploited in production. It covers prompt injections, jailbreaks, data extraction attempts, and multi-turn agentic attacks, simulating real threats in a controlled environment before deployment.
How do you secure MCP servers?
To secure MCP servers, route all tool calls through an AI gateway that enforces which agents can invoke which tools, validates inputs for prompt injection, applies rate limits per consumer, and logs every tool invocation. Pair gateway enforcement with runtime guardrails that inspect tool outputs for sensitive data before they return to the agent.
How do you secure LLM agents in production?
Securing LLM agents in production requires four layers: an agent gateway to control which models, tools, and data sources they can access; runtime security to inspect every request and response; adversarial testing before each release; and continuous monitoring with alerts. Each layer addresses a different threat surface, access control, live threats, unknown vulnerabilities, and drift.