AI-Driven Data Governance Using Model Context Protocol for Privacy Compliance in Cloud-Native Enterprise Applications

Abstract

When cloud-native enterprise architecture intersects with artificial intelligence (AI), data privacy and the responsible use of sensitive data have become among the most challenging tasks in data governance. This paper presents a unified approach to AI data governance via the Model Context Protocol (MCP), a protocol controlling the contextual data fed to AI models during runtime. The proposed solution implements smart data classification, continuous monitoring, role-based data access enforcement, and output validation in cloud-native environments while maintaining compliance with international privacy regulations. Two additions sharpen the contribution: (i) a comparative analysis positioning MCP against role-based access control (RBAC), attribute-based access control (ABAC), Zero Trust architecture (ZTA), and data loss prevention (DLP) across granularity, context-awareness, AI-specific guardrails, and output-validation coverage; and (ii) a quantitative evaluation envelope anchoring MCP’s expected latency overhead, data-exposure reduction, and policy-enforcement accuracy to peer-reviewed benchmarks of XACML policy decision points, distributed ABAC evaluators, and ML-augmented DLP systems.