The Model Context Protocol (MCP) is rapidly becoming the standard way to connect AI agents with tools, APIs, enterprise systems and knowledge sources. Building those integrations is often surprisingly easy. Operating them securely in a real enterprise environment is not.

As organizations move from AI prototypes to production systems, a new set of questions emerges. Which tools should an agent be allowed to use? How are permissions enforced? How can organizations prevent agents from gaining excessive privileges? And how do existing identity and access management concepts translate into a world where software agents act on behalf of users?

This session explores the governance, security and architectural challenges that arise when AI agents gain access to enterprise systems through MCP. We will discuss authorization models, agent identity, delegated permissions, least-privilege approaches, auditing, tool governance and emerging architectural patterns for secure enterprise AI platforms.

Rather than focusing on a specific vendor implementation, the session provides a practical framework for designing MCP-based ecosystems that remain secure, manageable and scalable as the number of agents and connected systems grows.

Key Takeaways
• Understand the governance challenges introduced by MCP and AI agents
• Learn how authentication and authorization principles evolve in agent-based systems
• Explore architectural patterns for secure MCP deployments
• Understand how to prevent privilege escalation and uncontrolled tool access
• Learn practical approaches for building scalable and governable AI platforms

Target audience:
Software Architects, AI Engineers, Platform Engineers, Security Professionals and Technical Decision Makers.

Session duration:
45 minutes