Building trust with your customers starts with securely handling their data. Whether you're processing claims, underwriting policies, or training AI models for risk assessment, you’re working with deeply personal and sensitive data—personally identifiable information (PII), protected health information (PHI), and financial records that need to stay protected. But that same data is also essential for software development and innovation.
To develop and test new applications, you need realistic datasets that behave like production—but exposing raw data in non-prod environments is a major risk. Insurance data masking offers a practical solution: it transforms production data into safe, de-identified versions that retain your data’s format, structure, and utility. Currently, it's estimated that over half of all organizations use static data masking to protect non-production data.
In this guide, we’ll break down how data masking works, which techniques matter most in insurance, and how to use them to stay compliant while moving fast.
What is data masking?
Insurance data masking is the process of transforming sensitive data into a protected version that retains its format and utility but conceals the original values. This allows teams to use realistic data in non-production environments—like dev, test, or training—without exposing customer information.
Certain types of data collected from insurance customers are particularly sensitive and almost always subject to regulatory protection. Here’s a quick breakdown of the data you should prioritize for insurance data masking:
- Personally identifiable information (PII): Names, Social Security numbers, addresses, phone numbers, and driver’s license numbers.
- Protected health information (PHI): Health conditions, treatments, and other data tied to a person’s medical history.
- Protected financial data: Bank account numbers, income, credit history, and policy payment records.
The data masking process
Sensitive data flows across your interconnected claims systems, policy admin platforms, CRM tools, and underwriting engines. To effectively mask your data, you need to understand what data needs to be protected, and how it must be transformed to avoid breaking the systems that depend on it.
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1. Detect sensitive data throughout a dataset
The first step is to identify all the fields and columns that contain sensitive or regulated data. These values might appear across multiple tables and even in semi-structured formats like JSON fields or document stores.
2. Configure data transformation by data type and privacy requirements
Once sensitive fields are identified, you’ll configure how each one should be masked based on its data type and intended use. For example, you might scramble or shuffle names and addresses but preserve ZIP code format for testing location-based logic.
3. Execute data masking techniques
Now that your rules are defined, your masking engine processes the source dataset, transforming the sensitive fields while keeping everything else intact. Ideally, your solution enables you to maintain referential integrity, especially in multi-table schemas. For example, if a user ID appears in five different places across your database, every instance should be replaced consistently to avoid failures in automated test suites.
4. Audit the masked data to ensure functionality and compliance
Finally, the masked dataset should be validated before being used in downstream environments. Some teams run smoke tests or functional test suites against the masked data to confirm that applications still behave as expected. You can also compare statistical distributions or use differential privacy checks to verify the fidelity of the transformed dataset.
Masking techniques for insurance data
Below are the most common insurance data masking methods:
Redaction
Redaction replaces sensitive fields with nulls, blanks, or placeholder values, making the data unreadable and irreversible. It's best for fields not used in logic, like names or SSNs during UI or layout testing. In insurance apps, redacting too aggressively can break validation logic, so use it selectively.
Scrambling
Scrambling jumbles characters within a field, preserving structure but destroying meaning. It’s fast and irreversible, useful for short phrases or license numbers where field length and character types matter.
Shuffling
Shuffling keeps real values but reassigns them across records, breaking row-level relationships. This technique works well for categorical data like marital status or job title.
Statistical replacement
This technique swaps values with synthetic ones that match the original data’s statistical profile. It’s a solid choice for AI model training or testing rare event scenarios—like catastrophic claims or outlier fraud cases—without exposing actual policyholder data.
Format-preserving encryption
Similar to standard encryption, format-preserving encryption converts sensitive values into code that can only be deciphered with an encryption key—but goes beyond basic encryption by ensuring the encrypted values and original values share the same format. This protects the security of the data while also providing utility.
Using data masking to meet regulatory requirements
Insurance companies must comply with a complex patchwork of data privacy regulations. Here’s a quick overview of the most relevant for the insurance industry:
- Insurance data masking and the California Consumer Privacy Act (CCPA): Masking personal information like names, addresses, and policy numbers helps ensure that non-production environments don’t violate California’s consumer privacy rights or trigger disclosure obligations.
- Insurance data masking and the General Data Protection Regulation (GDPR): By irreversibly anonymizing EU customer data, masking supports “data minimization” and “privacy by design” principles, especially when sharing across teams or systems.
- Insurance data masking and the Health Insurance Portability and Accountability Act (HIPAA): Replacing PHI with de-identified or Safe Harbor-compliant values enables healthcare insurers to test systems and train models without breaching protected health data rules.
- Gramm-Leach-Bliley Act (GLBA) & Payment Card Industry Data Security Standard (PCI DSS): Masking supports secure handling of financial data—like bank account numbers or card details—in ways that meet both banking privacy standards and payment card storage requirements.
Insurance case study
Kin Insurance adopted Tonic.ai to replace sensitive production data with high-fidelity, masked insurance data so they could test new product experiences and support rapid development. With 100% de-identified test data generated in under one hour, Kin eliminated manual test data workflows and unlocked faster iteration across development teams. The result: a streamlined, compliant data pipeline that helped support the insurance company’s 3x growth.
Explore Tonic.ai's solutions for insurance data masking
Insurance teams need more than just compliant data—they need high-fidelity datasets that behave like production, support rapid iteration, and protect customer trust. Tonic.ai delivers exactly that with platforms purpose-built for developers and data scientists. Whether you're testing claims logic, training AI models, or provisioning data environments, Tonic.ai helps you move faster without compromising privacy.
Ready to build with better data? Book a demo to see how Tonic.ai can streamline your test data workflows.
