AI agents are changing the enterprise security equation. An agent goes beyond responding to questions to actually take actions and execute tasks on your behalf. As part of that process, it may query sensitive data, call tools, trigger workflows, write code, change configurations and move information across systems at high scale and speed. That is what makes agentic AI so powerful — and difficult for traditional security models to handle.
Enterprise security teams are no longer asking, "Can we provide people access to AI?" Now they're asking, "How do we make sure AI agents are executing tasks safely and on behalf of the business?"
At Snowflake, we believe the answer starts with a simple principle: agentic AI must be governed where enterprise data, context and controls already live. Security cannot be bolted on after an agent is deployed. It has to be built into the model, the agent runtime and the data layer from the start.
That is the foundation of Snowflake’s Data-Model-Agent security framework.
Why agentic AI needs a new security model
