Model Context Protocol (MCP): The new standard for AI integrations in your organization explained

The Model Context Protocol (MCP) is an open-source standard launched in 2024 to securely connect AI models with your corporate data. The protocol acts as a universal data plug, eliminating the need to build vulnerable custom integrations for each application. This allows AI systems to operate efficiently across various platforms without the risk of vendor lock-in.

In short

MCP is a universal protocol that securely connects AI models to your business applications and files.
It replaces fragmented, hard-to-maintain custom integrations with a single standardized approach.
Because the technology is open source, it is widely supported by major software vendors and AI providers.
You retain full control over data access rights, which is essential for compliance with the AI Act.

What is an AI agent and what does mcp add to it?

Before we dive into the technology, it is important to answer the frequently asked question 'what is an AI agent'. Unlike a simple chatbot that exclusively generates text, an AI agent is a software system that independently takes steps, reasons and executes actions to achieve a predetermined goal. Think of reading a voluminous file, consulting current legislation and subsequently preparing a draft decision independently.

The biggest limitation for these systems has always been the connection to reliable information. A Large Language Model (LLM) is fundamentally trained on historical data. To truly take work off your hands, the model needs direct access to your internal document management, CRM system or specialized databases. Previously, this required a unique, custom-built integration for each individual data source, specifically designed for the AI model of one particular vendor. This was not only expensive but also very maintenance-intensive.

The Model Context Protocol (MCP) completely changes this dynamic. It offers a universal, open standard to shape these connections. You can compare it to a USB-C connection, but for data integrations. The protocol ensures that the AI can talk to all your systems using a single universal language. You build the connection once, and then any AI agent that supports the protocol can access the information securely and in a structured manner.

Why is an open standard crucial for your organization?

Since its introduction by Anthropic in late 2024, the protocol has grown into the absolute market standard throughout 2025 and 2026. The foundation of this success is the open nature of the technology. An open standard is crucial for a future-proof IT architecture because it protects you against vendor lock-in. If you invest in data integrations for your AI today, you do not want that investment to become worthless if you decide to switch to a more powerful model from another provider next year.

In addition, a fragmented landscape of custom APIs entails significant security risks. With every unique connection, the security must be redesigned and tested. By working with a standardized protocol, a uniform layer is created across your IT landscape. You can configure security rules and access rights in one central location. This aligns closely with the principles for securely unlocking applications, such as those described in the ICT security guidelines for web applications by the Dutch National Cyber Security Centre (NCSC).

When an AI system requests source data, it always does so within the strictly defined frameworks of the MCP server. This significantly reduces the attack surface of your organization. Furthermore, this transparent way of data sharing simplifies demonstrating correct data processing for the compliance requirements of the AI Act.

How does MCP differ from traditional integrations?

To fully understand the impact of the Model Context Protocol, it is useful to compare it with the traditional way applications communicate through Application Programming Interfaces (APIs).

Feature	Traditional API connection	Model Context Protocol (MCP)
Maintenance	High: each connection requires unique customization.	Low: one universal standard for all AI models.
Security	Fragmented: settings differ per connection.	Central: you manage access rights in one place.
Vendor lock-in	High risk: switching to another AI model is expensive.	No risk: the standard is open source and widely supported.
Application	Suitable for specific, fixed data streams.	Optimized for dynamic AI agents and large contexts.

What opportunities does this offer for agentic AI and your business processes?

With the technological hurdles removed, you can focus on scaling AI applications. The shift from isolated experiments to scalable, agentic solutions fundamentally changes how organizations work. By focusing on agentic AI in organizations, you transform sluggish and repetitive tasks into fast, error-free processes.

A practical example is the handling of compliance checks. An agentic AI system can independently retrieve the necessary policy documents from your SharePoint via the Model Context Protocol, compare them with the most recent legal texts from an external database and then publish a summary report in your management system. Because the connections are standardized, this process is secure, traceable and occurs without human intervention during data processing. If you want a broader view on automating workflows with AI and low-code, read our article on whether you should choose an AI agent or Power Automate.

For a successful implementation, follow these steps:

Inventory the source systems: Accurately map out which business applications contain the most important data for your critical work processes.
Check data hygiene: Ensure that documents and databases are cleaned up, up to date and correctly labeled.
Determine access rights per role: Establish who, and therefore which virtual assistant, is allowed access to which sensitive information.
Analyze the existing IT architecture: Assess with your IT partner whether the current environment can facilitate running a standardized server for these protocols.

How it works at Prudai

For AI agents to reason successfully, a flawless source architecture is indispensable. Our method: one fact in one place, information alignment and source referencing. LEO, VERA, ZIA, BEVER and IRMA work on one shared source registry with 158 validated sources. 76 of the sources are live connections: requested in real-time from the source, not a copy.

Additionally, control in the process is essential for quality. Workflows in LEO and LEO Fiscaal follow a fixed pattern: intake, plan, human approval, parallel specialists, a counter-role (counterparty, inspector or regulator), reviewer, editor, deliverable.

Prudai is certified to ISO/IEC 27001:2022 and NEN 7510-1:2024. This ensures that we handle the infrastructure surrounding these types of connections in a secure and structured manner.

For the specialist

For technical professionals and IT architects, the underlying structure of the Model Context Protocol is highly interesting. The protocol is designed around an asynchronous client-server architecture. This means the client and server process tasks independently, which perfectly matches how Large Language Models operate.

An MCP host, which can be a local desktop application or a web platform, internally runs an MCP client. This client maintains a continuous connection (a stateful connection where context is preserved) with one or more MCP servers. For local use, communication often runs directly via the standard input and output of the operating system (stdio). External integrations usually use secure HTTP connections, combined with Server-Sent Events (SSE) to send real-time updates from the server to the client.

The standardization focuses on three core components:

Resources: This allows the model to read data files, database rows or API responses based on unique addresses (URIs).
Prompts: These make it possible to centrally manage context-rich, dynamic instruction templates.
Tools: These offer the LLM the ability to execute actions, whereby the protocol enforces predefined, strict data schemas (JSON) for the commands.

Through this strict separation of logic and execution, the architecture simplifies compliance with the ICT security guidelines for web applications by the NCSC. You no longer need to write and maintain vulnerable, customer-specific middleware. The MCP server exclusively shares the pre-validated Resources and Tools. This makes implementing zero-trust network principles significantly easier.

Frequently asked questions

What is the relationship between this protocol and the European AI Act?

The AI Act requires organizations to maintain strict and traceable data governance around AI systems. By using an open and standardized protocol, you can record in a central and auditable location which source data your AI models request and process. This makes it much more feasible to demonstrate compliance with the legislation.

Does the Model Context Protocol exclusively work with models from Anthropic?

No. Although the protocol was launched by Anthropic in late 2024, it was immediately introduced to the market as an open-source standard. Many other major AI developers and cloud providers have now embraced and integrated the standard, effectively protecting you from vendor lock-in.

How do you ensure the security of sensitive corporate data in these integrations?

The architecture guarantees that the integration server can run securely isolated within your own IT infrastructure. You define in detail which data (Resources) and actions (Tools) you share with the AI model. Because the access flow goes through this single controlled access point, you prevent unauthorized data extraction by external AI agents.

Sources

Would you like to know how the right data architecture and secure protocols accelerate your transition to AI? View the AI services of Prudai and discover the possibilities for your organization.

Updated on 16 October 2026