As organizations scale agentic AI use cases, they need a way to grant access dynamically without sacrificing privacy or compliance. Data teams need workflows deliver that balance — automating access where appropriate, and enforcing human oversight as needed.
In part two of our series on solving data provisioning and privacy in AI use cases, Alan Clarke, Senior Solutions Architect at Immuta, shows how to use data products to control and audit AI agents‘ access to both structured and unstructured data.
You’ll see how to:
- Wrap unstructured loan documents in Amazon S3 into an Immuta data product
- Create a Snowflake data product for sensitive transaction and credit history data
- Apply different governance models based on data sensitivity, including time-bound access and human-in-the-loop approval processes
- Maintain full auditability and control over how AI agents request, use, and lose access to data
- Compartmentalize data into products that enable scalable AI governance
See part one of this series: Introduction to AI Use Cases.