Model Context Protocol (MCP): The Emerging Standard Connecting AI to the Real World

Introduction

As artificial intelligence continues to integrate into modern business operations, the need for a standardized way to connect AI models with real-world systems becomes more critical. The Model Context Protocol (MCP) addresses this challenge — an open standard that enables large language models (LLMs) and intelligent agents to interact securely with external tools, data sources, and services ¹. MCP essentially allows AI systems like ChatGPT, Claude, or other LLMs to go beyond static responses and perform real, contextual actions across digital environments.

What Is MCP?

According to CyberArk ², the Model Context Protocol is a standardized framework designed to help AI agents and LLMs interact seamlessly with external systems such as APIs, cloud services, and enterprise databases. It acts as a bridge between artificial intelligence and the real world, providing a secure and consistent way for models to access, read, and execute data or actions through structured interfaces. In essence, MCP turns a language model from a passive conversational tool into an active agent capable of performing meaningful, auditable operations.

Why Does MCP Matter?

Traditional AI integrations are often ad hoc and lack standardization. MCP changes that by introducing a universal language that allows models to communicate with multiple systems without custom integration work ¹. This reduces development overhead and improves security posture through consistent governance and authentication layers. From a security perspective — as Red Hat emphasizes ³ — MCP lays the foundation for secure AI interoperability. It ensures that every connection between an AI model and external resources is authenticated, authorized, and monitored, preventing context leakage, privilege escalation, and data misuse ³.

Who Created and Uses MCP?

MCP originated as an open initiative from Anthropic, designed to complement the ecosystem of tools surrounding models like Claude. Since its introduction ¹, it has been adopted and supported by key players such as Red Hat ³, CyberArk², and members of the broader open-source community. Developers, AI engineers, and cybersecurity professionals are increasingly leveraging MCP to build trustworthy AI agents that operate safely in enterprise environments, especially across hybrid and multi-cloud infrastructures.

When Is MCP Becoming Relevant?

Introduced in 2024¹, MCP gained rapid attention as organizations recognized the risks of uncontrolled AI integrations. Entering 2025, its relevance has grown significantly as businesses seek to integrate AI into mission-critical workflows — from automated security monitoring to intelligent DevSecOps pipelines. MCP enables these integrations to happen securely, predictably, and in compliance with enterprise policies^{2 3}.

Where Is MCP Being Applied?

MCP is being implemented in a wide range of contexts: from AI development environments and cloud orchestration to enterprise automation, cybersecurity operations, and identity governance ³. As Red Hat highlights, MCP helps maintain consistent security controls across distributed AI ecosystems ³. By isolating model contexts and managing permissions centrally, it ensures sensitive data remains protected, even when multiple AI agents are running in parallel.

How Does MCP Work and Stay Secure?

MCP operates through a client-host-server architecture:

• The client is the AI model or agent requesting information or performing actions.
• The host mediates communication, enforces security policies, and validates permissions.
• The server exposes external tools, APIs, or datasets that can be safely accessed.

CyberArk stresses the importance of identity-based controls, context isolation, and full audit trails for MCP deployments. Red Hat reinforces that a zero-trust approach and least-privilege principles must be applied to prevent unauthorized access or data leakage between AI sessions.

Objective

The primary goal of the Model Context Protocol is to enable secure, standardized interoperability between AI and the systems it interacts with. MCP aims to make intelligent agents not only powerful but also transparent, governable, and compliant with modern cybersecurity principles. It defines how models can safely “see,” “understand,” and “act” within complex digital environments.

Benefits

Organizations adopting MCP gain multiple strategic benefits:

• Cross-platform interoperability, reducing integration time and vendor lock-in.
• Enhanced security and visibility for every AI-initiated action.
• Improved development efficiency for DevSecOps and AI engineering teams.
• Compliance and governance assurance, aligning AI usage with enterprise policies. Ultimately, MCP allows businesses to embrace AI automation without compromising on control, oversight, or trust.

Conclusion

The Model Context Protocol represents a pivotal step toward a future where artificial intelligence is both intelligent and secure. It transforms LLMs from passive models into context-aware, action-driven systems that operate safely within enterprise boundaries. For cybersecurity professionals, understanding MCP is not optional — it’s essential. As AI becomes more deeply embedded into infrastructure and decision-making, MCP provides the blueprint for connecting innovation with integrity, ensuring that the rise of intelligent agents does not come at the expense of security or governance.

Note: This article reflects my personal views on AI, MCP, and automation in the context of cybersecurity. Parts of the writing were assisted by LLM AI to help minimize typos.

References

1. “What Is the Model Context Protocol (MCP)? - Model Context Protocol.” Modelcontextprotocol.io, Model Context Protocol, 2025, modelcontextprotocol.io/docs/getting-started/intro. ↩︎ ↩︎² ↩︎³ ↩︎⁴

2. “Frequently Asked Questions on Model Context Protocol (MCP).” CyberArk, 2025, www.cyberark.com/what-is/model-context-protocol/. ↩︎ ↩︎² ↩︎³

3. “Model Context Protocol (MCP): Understanding Security Risks and Controls.” Redhat.com, 2025, www.redhat.com/en/blog/ ↩︎ ↩︎² ↩︎³ ↩︎⁴ ↩︎⁵ ↩︎⁶