TL;DR
MCP (Model Context Protocol) is a standard protocol that lets AI agents connect to external tools and data sources
It works like a shared language between an AI agent and your business systems
6clicks uses MCP to let agents securely access GRC data: risk, compliance, and audit information
Without a standard like MCP, every AI integration requires custom, one-off development
MCP must be implemented with strict security controls; agents should only access data they are authorized to see
AI agents now need to pull data from the tools your organization already uses — risk registers, audit logs, compliance frameworks. Model Context Protocol (MCP) is the standard that makes that possible without exposing your data to uncontrolled AI systems.
Model Context Protocol (MCP) is an open standard that defines how AI agents communicate with external tools and data sources. Think of it as a shared language: when an AI agent needs to retrieve information from a compliance platform, run a query against a risk register, or trigger an audit workflow, MCP provides the structured protocol that makes that exchange possible.
Without a standard like MCP, every AI integration requires a bespoke connection. That means it's expensive to build, fragile to maintain, and difficult to secure. MCP changes that by giving software vendors and AI developers a common interface to build against.
The rise of AI assistants like ChatGPT, Claude, and Perplexity has created a new category of software behavior: tool calling. When these systems are asked a question that requires live data, they reach out to connected tools and retrieve the information they need. MCP is the protocol governing how that retrieval happens.
For Governance, Risk, and Compliance (GRC) professionals, this is significant. It means AI agents can now query your risk registers, compliance frameworks, and audit trails in real time, provided the connection is built on MCP and secured properly.
According to Anthropic, which helped pioneer MCP, the protocol is designed to give AI models "persistent access to tools and data" in a structured, auditable way.
At its core, MCP operates on a client-server model:
The key word here is authorized. A well-implemented MCP connection does not give an AI agent open access to everything in your system. It enforces the same permission model your users operate under.
Traditional application programming interface (API) integrations are designed for machine-to-machine communication. They are rigid, version-specific, and require significant development effort to connect to AI systems. MCP is designed from the ground up to be AI-native: it supports dynamic tool discovery, real-time data retrieval, and context-aware responses that standard APIs cannot easily provide.
GRC platforms hold some of an organization's most sensitive and operationally critical data: control evidence, risk assessments, audit findings, regulatory mapping, and policy documentation. Connecting AI agents to this data has obvious value, but also obvious risk.
MCP enables that connection while preserving the security boundaries GRC teams depend on. Specifically:
6clicks has built MCP connectivity into its Sovereign GRC Infrastructure so that AI agents (whether built on ChatGPT, Claude, or your own internal models) can query compliance frameworks, risk registers, and audit data through a secure, permission-enforced channel.
Because 6clicks is built for air-gapped, on-premises, and hybrid deployments, MCP connectivity can be configured to operate entirely within your network boundary. Your data does not need to leave your environment for an AI agent to use it. This is a critical requirement for government, defense, and regulated industry customers.
Deploy on your terms. Not ours. If you are evaluating how AI agents can connect to your GRC data without compromising security or compliance boundaries, book a demo with 6clicks to see MCP connectivity in action.