Lunch and Learn: Securing your Model Context Protocol Footprint

Leaders across industries are using the Model Context Protocol (MCP) to put tools in the hands of their AI agents, so they can do real work and drive measurable return on AI investments. However, it’s critical that MCP be implemented with guardrails to protect against malicious actors and unintended actions. Stacklok will show you how to use its open source ToolHive MCP platform to curate a trusted MCP registry and implement auth, token exchanges and other security best practices so you can put MCP into production

Model Context Protocol (MCP) Overview

MCP is an open standard developed by Anthropic that provides a universal way for AI models to connect with external data sources, tools, and services. Think of it as a "USB-C port for AI"—a single, standardized protocol that replaces the need for custom integrations between every AI application and every data source. MCP uses a client-server architecture where AI-powered applications (clients) connect to lightweight MCP servers that expose specific capabilities like database access, API integrations, or file system operations. This means developers can build one integration that works across any MCP-compatible AI platform, dramatically reducing the fragmentation of the current ecosystem. For businesses, MCP enables AI assistants to securely access real-time company data—from Slack and Google Drive to CRMs and internal databases—without sacrificing control or security. It's rapidly gaining adoption across the industry, with companies like Google, Microsoft, Salesforce, and many others building MCP support into their platforms.