Enterprise Scale Privacy Preserving Data Fabric Architecture for Multi Tenant AI Driven Customer Relationship Management Platforms

Multi-tenant customer relationship management (CRM) platforms increasingly use artificial intelligence for churn prediction, complaint analysis, campaign scoring, lead prioritization, and service decision support. At the same time, these platforms must protect tenant data, control access across shared infrastructure, and maintain governance visibility for enterprise deployment. This paper proposes an enterprise-scale privacy-preserving data fabric architecture for multi-tenant AI-driven CRM platforms. The framework combines tenant-partitioned data storage, a shared metadata and policy control plane, federated learning, identity-centered access control, governed feature publication, and audit linked AI orchestration. The proposed architecture supports cross-tenant analytical intelligence without pooling raw customer data into a centralized repository. The study presents the system design, multi-tenant data flow, privacy control model, AI workflow, and evaluation structure for a prototype CRM environment containing structured and unstructured customer interaction data. The results indicate that the architecture maintains tenant isolation, supports shared model learning through protected updates, and provides governance-aware AI services across operational and analytical CRM tasks. The discussion also shows that the framework offers a practical alternative to conventional centralized CRM warehouses, which often face higher exposure risk, weaker tenant separation, and fragmented governance control. Overall, the paper presents a unified architectural approach that connects privacy preservation, multi tenant intelligence, policy aware access control, and enterprise CRM decision support within one coordinated system.