> For the complete documentation index, see [llms.txt](https://docs.trustlogix.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.trustlogix.io/trust-ai/ai-agents.md). # AI Agents The AI Agent module in TrustLogix is called TrustAI. It is a core component of the TrustLogix Data Security Platform designed to secure and govern data access by Artificial Intelligence (AI) agents and pipelines. TrustAI ensures that sensitive data remains secure and compliant even as organizations adopt and scale their AI initiatives. The AI Agent module focuses on providing dynamic, policy-based data security specifically for AI systems. Its primary functions include: * Enforcing Business-Aware Access Control: TrustAI utilizes Attribute-Based Access Control (ABAC) policies to define fine-grained access for AI agents. * Dynamic Policy Enforcement: When an AI agent requests data, TrustAI's core intelligence, via a Policy Decision Engine (PDE) exposed through the TrustLogix MCP Server, determines the entitlements in real-time. The server returns a decision (permit, deny, or conditionally allow), which the AI agent then uses to rewrite, filter, or constrain its data query before execution. * Preventing Unintended Data Leakage: By applying data sensitivity policies automatically during AI training and inference, TrustAI ensures that sensitive data is protected. This is crucial for maintaining a least-privilege security posture for AI workflows. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.trustlogix.io/trust-ai/ai-agents.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.