Securing AI Agents With MCP: Field-Level Security in 2026

Authorization is optional in Model Context Protocol (MCP), creating security risks when AI agents access enterprise data through broad permissions, shared credentials, or inadequately scoped tools. The guide argues that conventional access controls designed for human users are insufficient for agents, which can chain actions across systems and expose information through tool calls, APIs, or outputs. It recommends enforcing field-, row-, and table-level permissions at query time through identity passthrough, where source systems apply each requesting user’s existing OAuth or SAML entitlements rather than relying on service accounts or forwarding raw tokens, a practice MCP prohibits. Least-privilege controls should also isolate workspaces, limit available tools and operations, use short-lived scoped credentials, validate token audiences, and authorize every request independently. Detailed per-query audit logs linking user identity, actions, systems accessed, returned data, and timestamps are presented as important for incident response and compliance with ISO 27001, SOC 2, and GDPR. CData Connect AI is described as a platform implementing these practices through identity passthrough, workspace and toolkit boundaries, source-system enforcement, and audit logging.

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