From Identity Vacuum to Identity Driven Access Control: Securing LLM Agents in the Enterprise

As enterprises integrate large language models (LLMs) with internal data sources using the Model Context Protocol (MCP), a crucial challenge arises in maintaining data security and access control, which is often overlooked. The article discusses the potential risks of data leakage, compliance violations, and trust erosion when identity-aware access control is not implemented, as MCP can allow unrestricted access to sensitive data. The solution proposed involves shifting from an identity vacuum to identity-driven access control by passing user-specific identity tokens through the MCP server to the data layer, using tools like Neo4j MCP Server and Keycloak for authentication. This approach ensures that, depending on their roles, users receive tailored access to data, thereby maintaining strict data governance and sovereignty. This method of secure access control is crucial for enterprises aiming to leverage AI while upholding data security and compliance standards.

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