What is Model Context Protocol (MCP)?

Bringing identity, permissions, and policy to AI workflows that touch enterprise data

Apr 21, 2025

The way AI models access enterprise data is changing fast. A single AI query can now touch dozens of apps—Workday, Salesforce, Slack, Gmail—across different teams and data sources. This creates a new security and permissioning challenge that traditional identity and API controls can’t handle.

What MCP Actually Does

Model Context Protocol (MCP) is an emerging specification that solves this. It ensures that models operate with the right identity, the right access, and with full auditability, even in complex multi-tool workflows. MCP acts like an operating system for AI data access. It gives AI models the rules of engagement when interacting with enterprise systems. Specifically, MCP:

Tracks Who’s Asking: Every AI request is tied to a real user or service account, so you always know who’s behind the query.
Filters What’s Visible: The model only sees data the user is authorized to see—no more, no less.
Orchestrates Complex Actions: It handles sequences of steps—like fetching files, calling APIs, and updating systems—while maintaining context across tools.
Applies Enterprise Rules: Policies like “Finance data can’t leave the US” or “Only managers can view salaries” are enforced by design.
Logs Everything: Every data access and decision made by the model is recorded, making it easy to audit or debug.

Source: Descope’s piece on MCP [link]

The Data Permissioning Challenge

In traditional SaaS:

Users authenticate into one app (e.g., Workday).
Permissions and data access are tightly scoped.

In modern AI:

A user asks a model like OpenAI to answer a question.
The model queries multiple systems (Workday, Salesforce, Jira, etc).
But user permissions are unclear, and access control is often bypassed.

Why AI needs a data access layer

In AI-forward orgs, API-based data requests exceed human ones by over 10x (47% of surveyed orgs).
API data retrieval is expected to double in the next 12 months.
Without identity-aware access control for these AI-triggered API calls, data leaks become inevitable.

Example of MCP-based AI agent request

Imagine you tell an AI voice assistant (think a customer service rep) for Delta Airlines that you want to change your flight:

"I want to change my flight to 9 a.m. tomorrow"

That one request then needs to touch the right tools to actually execute the workflow and change your flight. The first step is mapping out the tool requests:

Speech-to-text (Deepgram)
Intent detection (OpenAI)
Authentication (Auth0, Okta)
Flight pricing APIs (Amadeus, Sabre)
Payment (FIS)
Notifications (Twilio)

MCP sits between all of them, ensuring permissions, audit logs, and correct execution. Without MCP, any one step could access the wrong data or act outside user intent.

Adoption Trends of MCP

Model Context Protocol was introduced by Anthropic in November 2024 and quickly gained traction, especially after OpenAI integrated it into its Agents SDK in March 2025. Other industry players adopting MCP include:

Google DeepMind and Microsoft have since joined the ecosystem, with Google confirming MCP support for its Gemini models and Microsoft integrating MCP into Azure Copilot.
Developer-centric platforms like Replit and Sourcegraph have adopted MCP to enhance real-time codebase access and streamline software development.
Block (formerly Square) have leveraged MCP to securely link internal AI systems to proprietary data.
Cursor has also added MCP to connect internal tools and data to its application.

As of mid-2025, over 1,000 community-built connectors are available, making MCP an increasingly central standard for enterprise AI integrations. Industry analysts estimate that over 20% of enterprise AI deployments by the end of 2025 will utilize MCP or similar frameworks, highlighting its rapid adoption and strategic importance in secure AI workflows.

Why MCP Matters

MCP turns AI from a rogue tool into a trusted enterprise agent:

✅ Security: Stops over-permissive data access
✅ Compliance: Ties every request to identity and policy
✅ Scalability: Works across all apps, APIs, and users

In short, MCP is the missing glue between LLMs and real enterprise workflows.

TL;DR: Model Context Protocol (MCP)

As the AI stack matures, Model Context Protocol is becoming foundational—much like OAuth was for web apps.