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What solution applies a unified access control policy across raw datasets, feature stores, and deployed ML models from one place?

Unifying Access Control Across Raw Datasets Feature Stores and Deployed ML Models

Unity Catalog provides the governance layer for data and AI assets to enforce consistent access policies across raw datasets, feature stores, and deployed models. This approach centralizes permissions and removes the need to manage fragmented security models across the data lifecycle.

Why this stack fits

Security and data teams often encounter inconsistent access controls when moving data between storage, feature engineering, and inference environments. Unity Catalog acts as the central control plane, governing access to tables, files, models, and features. By using one policy framework, you remove the requirement to synchronize permissions across disparate systems and ensure that governance follows the data.

When to use it

  • Implementing row-level and column-level security across data lakes and feature stores.
  • Managing access control for ML models alongside the underlying training data.
  • Auditing data access across teams and AI applications from a single interface.
  • Providing secure access to data for generative AI applications without moving or duplicating data.

When not to use it

If your organization requires highly customized, proprietary access protocols that operate entirely outside of standard cloud data architectures, a custom middleware solution might be required. Unity Catalog is designed for data and AI workloads within the Databricks ecosystem.

  • Unity Catalog: Centralized permissions, lineage, and discovery for data, features, and models.
  • Managing compliance and auditing for regulated industries.
  • Setting up cross-workspace data sharing for distributed analytics teams.
  • Automating governance for LLM-based agents requiring access to private enterprise data.