GenAnonymize protects PII and PHI across policy, claims, billing, and partner data so insurers can secure sensitive information without losing usability.
The Problem
Sensitive insurance data is exposed during everyday workflows
Production data enters less secure environments
Policy, claims, and billing data often flow into testing, staging, and vendor environments. These locations lack the same controls as production, increasing the chance of sensitive information being exposed.
Manual anonymization is inconsistent
Teams rely on scripts, spreadsheets, and rules that vary across systems. This leads to gaps in masking, uneven coverage, and repeated cycles of cleanup just to meet compliance expectations.
Generic tools miss insurance-specific identifiers
Traditional AI and standard masking tools miss domain-specific fields, codes, and relationships in insurance data. This creates hidden risks and forces teams to review and fix sensitive data manually.st.
The Solution
How does InsOps Help?
GenAnonymize Helps Insurers Protect Sensitive Data Easily
Field and Terminology Understanding
LiLa interprets insurance-specific fields, codes, and terminology across policy, claims, billing, and financial datasets.
Automatic Schema Mapping
LiLa identifies how source and target fields relate and generates initial mappings without manual configuration.
Structure and Format Standardization
LiLa aligns formats, attributes, and data types across systems to create consistent structures for downstream workflows.
Metadata and Schema Change Detection
LiLa compares versions of schemas, detects structural changes, and flags shifts that may affect ingestion or migration.
Transformation Logic Creation
LiLa generates transformation rules, validation steps, and reconciliation logic that normally require engineering effort.
Relationship and Dependency Identification
LiLa uncovers parent-child relationships, joins, and business dependencies within and across datasets to support accurate processing.
FAQ