Immuta Agentic Access: Authorizing AI Data Use

AI agents move at machine speed.
Your access model should too.

AI agents are becoming a new class of data consumer. They operate continuously, make decisions quickly, and may take multiple actions to answer a single question. Traditional access models, built around shared accounts, impersonation, standing permissions, and ticket-based workflows, were not designed for this environment.

This whitepaper explains how delegation, data-plane enforcement, and just-in-time access create a more accountable model for AI data use. Learn how to govern agents as distinct identities, preserve the context of the users they act for, and enforce policy where data is actually accessed.

What’s inside

• ‍Why impersonation creates risk: How agents that inherit a user’s identity can receive broader permissions than the task requires, create rights inflation, and blur accountability.‍
• Delegation, not impersonation: How delegated authorization preserves the distinction between the human who initiates or authorizes the work and the agent that acts on their behalf.‍
• Why the data plane matters: The difference between the control plane, where an agent interprets intent and plans a task, and the data plane, where access policies must be enforced before data is returned.‍
• From standing access to just-in-time access: How temporary, task-scoped permissions can reduce standing exposure and account sprawl while giving agents the access they need to complete a governed task.‍
• A more accountable agentic architecture: How distinct identities, delegated authorization, temporary access, and dual-identity audit trails help security and compliance teams answer who requested the data, which agent acted, what data was accessed, why it was approved, and when the access occurred. ‍
• Where Immuta fits: How Immuta’s Agentic Data Access capability extends data provisioning to AI agents at the data plane, complementing the control-plane systems that orchestrate agent behavior.

Download the whitepaper to learn how to authorize AI data use without relying on impersonation or standing access.