Protecting Merged Data Strategies for Governance & Access Control
AI Summary
The video discusses the evolving use of data in enterprises, from traditional business intelligence to advanced AI applications like generative AI and retrieval-augmented generation models. It highlights how data from various sources across the enterprise is merged into unified systems such as data warehouses and vector databases to make querying and insights more accessible. The core challenge addressed is how to protect access to this merged data. The video outlines several strategies: maintaining strict access controls by treating merged data as a separate asset; ensuring users have comprehensive access rights if they are to retrieve responses from AI systems; concept of data objects for granular access; implementing data virtualization as a runtime access control layer; filtering query results pre or post access to maintain security; using birthright access based on user identity, role, and organizational context to simplify access permissions; and continuously ensuring compliance through observability and monitoring. Emphasis is placed on least privilege access and strong data governance to effectively secure and manage enterprise data merges in AI-powered environments.