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

The blog post by Bryan Lee, a Solutions Engineer at Neo4j, discusses the security challenges of connecting large language models (LLMs) to enterprise databases and the implementation of the Model Context Protocol (MCP) as a solution. MCP is likened to a "USB-C for AI" because it allows easy integration of AI models with various tools and services, but it poses significant security risks due to the lack of identity-aware access control. Without proper controls, sensitive data can be exposed to unauthorized users, leading to data leaks, compliance violations, and a loss of trust. The article proposes using Identity Driven Access Control to pass users' identities through the MCP layer to enforce role-based access controls at the database level, specifically in Neo4j. This approach uses OIDC authentication to ensure that each request carries the user's identity, allowing the database to enforce fine-grained access controls based on roles and maintaining data governance while connecting LLMs to enterprise data. The blog highlights the importance of this method for maintaining security and integrity in AI-driven workflows and offers practical guidance for implementing such a system.

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