Enterprise AI is moving fast. AI agents are no longer just experimental assistants; they are becoming a new way for people to query data, generate insights, automate workflows, and make decisions across the enterprise.
But as organizations move from AI experimentation to production, one question is becoming impossible to ignore: How do you give AI agents access to the data they need without giving them more access than they should have?
At Immuta, we are focused on helping enterprises solve that problem. We are sharing a closer look at three capabilities we have built to work with the Snowflake AI Data Cloud. These capabilities, powered by Snowflake, help joint customers govern AI agent access, provision data at machine speed, and make complex compliance environments easier to understand through natural language.
Together, they support trusted, policy-driven data access for every data consumer, whether that consumer is a human user, an application, or an AI agent.
For years, organizations have focused on improving AI model performance. Bigger models, better prompts, more sophisticated workflows, and faster infrastructure tend to dominate the conversation.
But in enterprise environments, the true bottleneck is often not the model itself. It is trust.
AI agents need access to data to be useful. But enterprise data is sensitive, regulated, fragmented, and governed by complex permission structures. Without strong access controls, AI agents can introduce serious risks, including unauthorized data exposure, compliance violations, and limited visibility into who, or what, accessed which data.
That is why agentic AI requires more than model intelligence. It requires governed access.
By combining Snowflake Cortex AI’s native query planning with Immuta’s policy-driven access provisioning, enterprises can support AI agents that are both capable and controlled. Agents can interact with data in natural language, while Immuta helps ensure that access remains scoped, temporary, auditable, and aligned to the authorizing user’s permissions.
That is what it means to provision data for the agentic era.
The capabilities Immuta has built with Snowflake are designed to help enterprises address some of the most urgent governance challenges emerging with agentic AI.
They focus on three critical areas: governing AI agent access, extending agent context beyond Snowflake, and simplifying compliance visibility through natural language.
Immuta Agentic Data Access, powered by Snowflake Cortex AI, enables AI agents to interact with enterprise data using natural language. Snowflake Cortex AI supports query planning, while Immuta enforces policy-driven access controls at the session level.
The key innovation is how access is provisioned for each agent interaction.
For every interaction, Immuta vends a unique, temporary role that is scoped to the user the agent is acting on behalf of. That means the AI agent does not receive broad or persistent access to enterprise data. Instead, access is dynamically provisioned based on what the authorizing user is permitted to see.
In practical terms, this helps ensure that an AI agent cannot access data beyond the permissions of the user it represents.
For enterprises, that control is essential. AI agents may be able to ask better questions, generate faster analysis, and automate complex workflows, but they still need to operate within established access boundaries. Immuta Agentic Data Access helps make that possible by applying policy controls directly to agent-driven data interactions.
As AI agents become more sophisticated, they will not only access data inside a single environment. They may need to interact with data and systems beyond Snowflake as part of broader enterprise workflows.
Immuta extends Snowflake’s agent principal contexts to govern outbound agentic access to data outside of Snowflake. This allows organizations to maintain governance as agents move across data environments and perform actions on behalf of users.
A key part of this capability is the use of zero standing privileges. Rather than granting agents persistent access, permissions are provisioned dynamically when needed. This reduces the risk of over-permissioned agents and helps limit exposure if credentials or workflows are misused.
The capability also captures every agent interaction in a dual-identity audit trail. This ties agent activity back to both the agent and the authorizing user, giving governance, compliance, and security teams clearer visibility into who initiated an action and how the agent interacted with data.
For enterprises deploying AI agents at scale, this kind of auditability is critical. Teams need to understand:
Immuta’s integration with Snowflake’s Agent Principal Context is designed to bring that level of accountability to agentic AI workflows.
Compliance and security teams often need to understand who has access to what data, how permissions are structured, and whether access aligns with internal policies and external regulations. But in large enterprise environments, permission structures can be complex and difficult to analyze.
The Immuta “Comply” App for Snowflake Horizon Catalog gives compliance and security teams a natural language interface for querying their Snowflake Horizon environment in plain English.
Instead of manually digging through complex access structures, users can ask questions and receive more transparent, searchable, and actionable insights.
This helps make compliance workflows faster and more accessible. It also bridges the gap between technical data governance structures and the teams responsible for oversight, risk management, and audit readiness.
AI agents introduce a new access pattern for enterprise data. They need to move quickly, interpret user intent, and interact with data on behalf of people, but they also need to stay within the same governance boundaries organizations already rely on.
That is the problem Immuta is solving with Snowflake. By pairing Snowflake’s AI and data capabilities with Immuta’s policy-driven access provisioning, joint customers can support agentic workflows without relying on broad standing privileges or manual access processes.
For business users, that means faster access to trusted data. For data, security, and compliance teams, it means dynamic controls, clear accountability, and auditability across both human and AI-driven activity.
As organizations bring AI agents into production, governed access becomes foundational. The goal is not just to make agents more capable. It is to make sure they operate with the right data, under the right permissions, and with the right level of oversight.
That is what Immuta has built to work with the Snowflake AI Data Cloud: a way to provision data for AI agents at enterprise speed, without losing control over who can access what.
Learn more about Immuta’s Agentic Data Access capabilities for the Snowflake AI Data Cloud on Snowflake Marketplace.
See how Immuta provisions temporary, policy-driven access for AI agents, so they can move fast without exceeding the permissions of the people they serve.
