Snowflake Integration for AI Agents
Let AI agents query Snowflake through a governed path that respects your row-access and masking policies. Agents generate analyses and reports from warehouse data, with results validated and cited, and nothing exfiltrated.
- Least-privilege access
- Human approval on writes
- Zero data retention
- Full audit trail
The ungoverned risk
Point an agent at Snowflake with a service account and it can read far more than the question needs. Raw rows flow into a model, row-access policies get bypassed, and a hallucinated number can land in a board report with no source.
What governed agents do
CreateOS routes Snowflake queries through the governed layer. Agents run scoped queries to build analyses, forecasts, and reports, honoring Snowflake row-access and column-masking policies, then validate the output and cite it back to the query that produced it.
What Agents Do in Snowflake
Every action is scoped to least privilege, validated, and logged. Anything that changes a record waits for a person.
Runs on the Unified AI Execution Layer
Agents inherit your Snowflake row-access and column-masking policies, queries are scoped to least privilege, outputs are validated and cited, and every query is logged so analysts can trace any number to its source.
Common Questions
Does this respect our Snowflake row-access policies?
Yes. Agents query under policies you already enforce in Snowflake, so row-access and column masking apply to agent queries exactly as they do to a person. CreateOS adds scoping, validation, and audit on top.
How do we trust a number an agent reports?
Every figure is cited back to the query that produced it, and outputs run through validation before they reach a person. An analyst can open the source query for any number in a report.
Can agents write to Snowflake?
By default agents read for analysis. Where a write is in scope, it is governed like any other action: least-privilege access, optional approval, and a full log of the statement that ran.
