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Reference workflows, reusable logic, and implementation guidance to discover sensitive columns, maintain a governed PHI/PII inventory, and automatically apply Unity Catalog column masks across a Databricks metastore.
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Healthcare, insurance, and other regulated data platforms often ingest data from many source systems. Each system may use different table structures and column naming conventions, making it difficult to manually track which columns contain PHI or PII.
DataTheta’s PHI/PII Column-Level Masking Accelerator helps teams scan Databricks catalogs, identify sensitive columns, maintain a central governance control table, and automatically apply Unity Catalog column masks without manual per-table ALTER TABLE work.
AI systems in production
Avg. time to first outcome
Forecast accuracy improvement
Faster decision cycles
Revenue influenced by AI
Manual processing eliminated
Scan catalogs, schemas, tables, and columns to identify likely PHI and PII fields using configurable keyword patterns.
Store flagged columns in a reviewable control table so data stewards can validate classifications and toggle masking decisions.
Create a reusable library of SQL masking functions that reveal real values only to authorized groups and redact them for other users.
Apply column masks automatically across flagged tables and columns without repetitive manual ALTER TABLE operations.
Run the masking workflow repeatedly, skip already masked columns, and surface unsupported data types for review instead of silently ignoring them.
Explore reusable accelerators for healthcare data privacy, governance, data quality, AI readiness, analytics automation, and enterprise decision systems.
Use DataTheta’s PHI/PII Column-Level Masking Accelerator to discover sensitive columns, govern masking decisions, and protect regulated data across Databricks and Unity Catalog environments.
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