MCP Dev Summit Mumbai 2026

Have you ever worried that your new AI Agent might be a little too helpful? Imagine an intern asking an HR chatbot, "What is the CEO's salary?" and the bot, designed to be helpful, promptly fetches it.

As we equip LLMs with tools via the MCP to query internal systems, we introduce a significant data privacy risk if the agent cannot distinguish who is making the request.

In this session, we’ll explore the critical intersection of IAM and AI agents. You’ll see how to implement fine-grained access control and “on-behalf-of” user execution in MCP servers. By securely propagating user context, an agent ensures that when the CEO queries salary data, the data is delivered, but when an intern makes the same request, the system restricts it.

We’ll also dive into the “Human-in-the-Loop” (HITL) pattern, a safeguard that pauses high-risk actions for explicit human approval. You’ll learn how to design secure-by-default MCP architectures using standard authentication flows, zero-trust permissioning, and HITL workflows to keep your AI agents aligned, controlled, and trustworthy.