Leveraging Model Context Protocol for Secure Cloud-Based Agentic Enterprise Integration and API Interoperability
DOI:
https://doi.org/10.15680/IJCTECE.2025.0805034Keywords:
Model Context Protocol, Agentic AI, Enterprise Integration, API Interoperability, Cloud Security, Zero Trust Architecture, Semantic WebAbstract
The rapid evolution of agentic artificial intelligence (AI) has introduced complex challenges in enterprise integration, particularly concerning secure data orchestration, API interoperability, and context maintenance across fragmented cloud ecosystems. This paper examines the deployment of the Model Context Protocol (MCP) as a standardized framework for establishing secure, bi-directional communication between LLM-based autonomous agents and enterprise data sources. By formalizing context exchange protocols, MCP mitigates common vulnerabilities associated with traditional webhook architectures and ad-hoc API wrappers, such as credential exposure, loose access controls, and contextual drift. We propose a robust architectural blueprint that integrates MCP within cloud-native environments, leveraging zero-trust network access (ZTNA), fine-grained tokenization, and dynamic schema mapping to ensure high-fidelity API interoperability. Through a comprehensive evaluation of data processing efficiency, latency overhead, and boundary security compliance, this research demonstrates how MCP-driven agentic workflows minimize semantic loss while enforcing strict enterprise data governance boundaries. Ultimately, this study provides actionable insights for enterprise architects aiming to scale secure, autonomous AI systems that interact seamlessly with legacy enterprise resource planning (ERP) platforms, customer relationship management (CRM) databases, and distributed microservices architectures without compromising structural data sovereignty.
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