how are you handling permission boundaries for internal data agents?

Reddit r/AI_Agents News
internal-data-agents permission-boundaries rbac llm-security bi-agent data-access read-write-permissions

Summary

The article discusses challenges in implementing role-based access control (RBAC) for an internal BI agent using LLM, addressing concerns about data leakage and write permissions for operational workflows.

we're building an internal BI agent that pulls from HubSpot CRM, QuickBooks, and a few internal PostgreSQL product databases and lets our leadership team query it in natural language. the prototype works in a sandbox, but as we get closer to production, our security and leadership teams are getting nervous. in a normal dashboard, access is rigid. with an LLM interface, someone asks "which accounts are at high risk of churn?" and the agent might pull sensitive margin data or contract values that person has no business seeing, even if they have basic CRM access. and another problem - management wants the agent to move from explaining data to acting on it. the moment we hand it write-permissions to operational workflows, the blast radius of a false positive skyrockets. how are you enforcing RBAC dynamically at the LLM layer without killing contextual flexibility? and where have you drawn the line between read-only and write-enabled?
Original Article

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