The AI engine that anonymizes insurance data securely
The AI engine that anonymizes insurance data securely
The AI engine that anonymizes insurance data securely
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
Insurers need to use real production data across development, testing, analytics, and reporting. To make this possible, data is copied into lower environments and connected to a growing set of internal and external tools.
Once sensitive data moves outside the core environment, visibility and control drop. Each copy increases exposure, and gaps often go unnoticed until audits, incidents, or breaches occur.
Most anonymization approaches are manual or rely on generic tools that do not understand insurance data or regulatory context.
This results in inconsistent protection, repeated rework, and delays, while still leaving sensitive information exposed across downstream systems.
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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.
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.
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.
Stage 1
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.
Comprehensive Consultation
Project Roadmap
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.
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.
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.
Stage 2
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.
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.
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.
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
AI that keeps sensitive insurance data secure and usable
AI that keeps sensitive insurance data secure and usable
GenAnonymize detects and anonymizes PII and PHI at the source, keeping policy, claims, billing, and partner data inside your environment. It preserves structure so insurers can run testing, analytics, and workflows safely.
Effortlessly connect with your favorite tools. Whether it's your CRM, email marketing platform.
How does InsOps Help?
GenAnonymize Helps Insurers Protect Sensitive Data Easily
GenAnonymize detects and anonymizes PII and PHI across policy, claims, billing, and partner data. It keeps all information inside your environment and preserves structure so teams can run testing, analytics, and workflows without exposure risk or manual cleanup.
Effortlessly connect with your favorite tools. Whether it's your CRM, email marketing platform.
Field and Terminology Understanding
LilaGPT interprets insurance-specific fields, codes, and terminology across policy, claims, billing, and financial datasets.
Field and Terminology Understanding
LilaGPT interprets insurance-specific fields, codes, and terminology across policy, claims, billing, and financial datasets.
Field and Terminology Understanding
LilaGPT interprets insurance-specific fields, codes, and terminology across policy, claims, billing, and financial datasets.
Field and Terminology Understanding
LilaGPT interprets insurance-specific fields, codes, and terminology across policy, claims, billing, and financial datasets.
Automatic Schema Mapping
LilaGPT identifies how source and target fields relate and generates initial mappings without manual configuration.
Automatic Schema Mapping
LilaGPT identifies how source and target fields relate and generates initial mappings without manual configuration.
Automatic Schema Mapping
LilaGPT identifies how source and target fields relate and generates initial mappings without manual configuration.
Automatic Schema Mapping
LilaGPT identifies how source and target fields relate and generates initial mappings without manual configuration.
Structure and Format Standardization
LilaGPT aligns formats, attributes, and data types across systems to create consistent structures for downstream workflows.
Structure and Format Standardization
LilaGPT aligns formats, attributes, and data types across systems to create consistent structures for downstream workflows.
Structure and Format Standardization
LilaGPT aligns formats, attributes, and data types across systems to create consistent structures for downstream workflows.
Structure and Format Standardization
LilaGPT aligns formats, attributes, and data types across systems to create consistent structures for downstream workflows.
Metadata and Schema Change Detection
LilaGPT compares versions of schemas, detects structural changes, and flags shifts that may affect ingestion or migration.
Metadata and Schema Change Detection
LilaGPT compares versions of schemas, detects structural changes, and flags shifts that may affect ingestion or migration.
Metadata and Schema Change Detection
LilaGPT compares versions of schemas, detects structural changes, and flags shifts that may affect ingestion or migration.
Metadata and Schema Change Detection
LilaGPT compares versions of schemas, detects structural changes, and flags shifts that may affect ingestion or migration.
Transformation Logic Creation
LilaGPT generates transformation rules, validation steps, and reconciliation logic that normally require engineering effort.
Transformation Logic Creation
LilaGPT generates transformation rules, validation steps, and reconciliation logic that normally require engineering effort.
Transformation Logic Creation
LilaGPT generates transformation rules, validation steps, and reconciliation logic that normally require engineering effort.
Transformation Logic Creation
LilaGPT generates transformation rules, validation steps, and reconciliation logic that normally require engineering effort.
Relationship and Dependency Identification
LilaGPT uncovers parent-child relationships, joins, and business dependencies within and across datasets to support accurate processing.
Relationship and Dependency Identification
LilaGPT uncovers parent-child relationships, joins, and business dependencies within and across datasets to support accurate processing.
Relationship and Dependency Identification
LilaGPT uncovers parent-child relationships, joins, and business dependencies within and across datasets to support accurate processing.
Relationship and Dependency Identification
LilaGPT uncovers parent-child relationships, joins, and business dependencies within and across datasets to support accurate processing.
Testimonial
What Our Insurance Partners Say
Hear how insurers accelerated data workflows, improved accuracy, and modernized operations with InsOps.
Effortlessly connect with your favorite tools. Whether it's your CRM, email marketing platform.
FAQ
Frequently
Asked Questions
Have questions? Our FAQ section has you covered with
quick answers to the most common inquiries.
Effortlessly connect with your favorite tools. Whether it's your CRM, email marketing platform.
What is GenAnonymize?
How does GenAnonymize anonymize data?
Does GenAnonymize work in real time?
What types of sensitive data can GenAnonymize detect?
Will anonymization impact data quality or usefulness?
Do we need to write scripts or manage rules?
Is GenAnonymize compliant with regulatory requirements?
Does any data leave our environment while using GenAnonymize?
Can anonymization methods be customized?
What is GenAnonymize?
How does GenAnonymize anonymize data?
Does GenAnonymize work in real time?
What types of sensitive data can GenAnonymize detect?
Will anonymization impact data quality or usefulness?
Do we need to write scripts or manage rules?
Is GenAnonymize compliant with regulatory requirements?
Does any data leave our environment while using GenAnonymize?
Can anonymization methods be customized?
What is GenAnonymize?
How does GenAnonymize anonymize data?
Does GenAnonymize work in real time?
What types of sensitive data can GenAnonymize detect?
Will anonymization impact data quality or usefulness?
Do we need to write scripts or manage rules?
Is GenAnonymize compliant with regulatory requirements?
Does any data leave our environment while using GenAnonymize?
Can anonymization methods be customized?
What is GenAnonymize?
How does GenAnonymize anonymize data?
Does GenAnonymize work in real time?
What types of sensitive data can GenAnonymize detect?
Will anonymization impact data quality or usefulness?
Do we need to write scripts or manage rules?
Is GenAnonymize compliant with regulatory requirements?
Does any data leave our environment while using GenAnonymize?
Can anonymization methods be customized?
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Every Insurer We Work With
Moves Faster and Operates Smarter.
Join us and turn complex data challenges into streamlined, automated operations with the help of our Insurance-trained SLM
Join Us Now
Every Insurer We Work With
Moves Faster and Operates Smarter.
Join us and turn complex data challenges into streamlined, automated operations with the help of our Insurance-trained SLM
Join Us Now
Each Project, Our
Design is Great.
Join us and turn complex data challenges into streamlined, automated operations with the help of our Insurance-trained SLM
Join Us Now
Every Insurer We Work With
Moves Faster and Operates Smarter.
Join us and turn complex data challenges into streamlined, automated operations with the help of our Insurance-trained SLM