The Definitive Guide to Connecting On-Premises Data to Claude and ChatGPT

Connecting on-premises data to cloud AI assistants such as Claude and ChatGPT requires overcoming network isolation, legacy-system limitations, and regulatory controls while ensuring that access, credentials, and activity remain governed. The Model Context Protocol (MCP) provides a standardized architecture in which an AI host communicates through an MCP client and server, with connectors translating approved requests to secured databases, APIs, and business systems without directly exposing those systems. Recommended implementation steps include classifying data, selecting an appropriate deployment model such as private links, VPNs, reverse proxies, or hybrid hosting, building narrowly scoped connectors, and enforcing short-lived credentials, role-based permissions, encryption, monitoring, DLP, and detailed audit logging. Organizations should also test retrieval accuracy and data redaction, establish approval, retention, and incident-response policies, and align controls with frameworks such as SOC 2, ISO 27001, and GDPR. Examples from TELUS and Bridgewater illustrate managed-cloud deployments of Claude, while CData Connect AI is presented as a managed MCP-based option that centralizes authentication, query translation, source-level permissions, and auditing for on-premises integrations.

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