LLM Data Access Architecture for Multi-System Enterprises in 2026

Multi-system LLM data access architecture determines how AI models securely retrieve current enterprise information from fragmented sources such as CRM, ERP, databases, and SaaS platforms, while controlling data movement, permissions, performance, and auditability. The text compares native Model Context Protocol (MCP) connectors, iPaaS synchronization, unified API or data federation, and centralized warehouse-based retrieval-augmented generation, emphasizing their trade-offs between live access, duplication, governance location, and scalability. It identifies prompt injection, excessive agent permissions, and inefficient cross-system data transfers as key risks, and recommends controls including identity passthrough, least-privilege role-based access control, request-level logging, and query pushdown to limit retrieved data and execute filtering or aggregation near the source. It argues that a centralized governed AI gateway or MCP endpoint can simplify management compared with many point-to-point connectors, while keeping credentials outside the model and requiring external authorization for consequential actions. The text presents CData Connect AI as a product offering these capabilities through prebuilt connectors, OAuth/SAML identity passthrough, RBAC, source-level pushdown, and auditing.

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