Modern organizations rely on sensitive data to operate, analyze, and innovate. At the same time, that data is accessed by many systems, teams, and partners across its lifecycle. Traditional security controls focus on who can reach a system, but not on who can actually use sensitive data once access is granted.

Encryption, tokenization, and masking are increasingly used to close this gap. They allow organizations to protect sensitive fields at the data layer while still enabling operational workflows, analytics, and AI. In practice, this means sensitive data can be broadly usable, without being broadly visible.

The use cases below reflect how organizations in this industry commonly apply these techniques to reduce risk, meet regulatory requirements, and safely enable data-driven use cases.

Insurance

Insurance companies manage large volumes of highly sensitive, long-lived data that spans personal identity, financial information, and medical records. This data is reused across underwriting, claims processing, actuarial analysis, fraud detection, customer service, and external partners.

The core challenge is that insurance data must remain accessible for decades, often across many systems and vendors, while exposure or misuse can create lasting regulatory, financial, and reputational damage.

Common data environments

Sensitive data in insurance environments typically exists across:

Policy administration and underwriting systems
Claims management platforms
Customer relationship and service systems
Fraud detection and investigation tools
Actuarial, pricing, and risk analytics platforms
Data warehouses and data lakes
BI, reporting, and regulatory systems
Third-party adjusters, providers, and partners

Common use cases

Field-level protection of policyholder and claimant data

Insurance providers encrypt or tokenize sensitive policyholder and claimant fields such as names, national identifiers, policy numbers, and medical indicators directly within operational databases. Protection is applied at the field level so core applications continue to function normally while sensitive values remain protected at rest and in use.

This limits exposure from insider access, application vulnerabilities, and data replication across systems without requiring application rewrites.

Identity-based access to full vs masked policy data

Different roles require different visibility into policy and claims data. Claims adjusters, underwriters, customer service agents, and analysts all access the same records but with varying levels of sensitivity.

Encryption and masking are used to dynamically return cleartext, partially masked, or fully protected values based on user identity and role. This ensures that each function sees only the data required to perform its job.

Tokenized analytics for actuarial and pricing models

Actuarial and pricing teams rely on large historical datasets to model risk and set premiums. Sensitive identifiers are tokenized before being ingested into analytics platforms, allowing joins, cohort analysis, and trend modeling without exposing real identities.

This enables broad analytical access while reducing regulatory exposure and breach risk in data warehouses and BI tools.

Protecting claims data across internal and external workflows

Claims data often flows across internal teams, third-party adjusters, legal partners, and service providers. Tokenization allows claims to be tracked consistently across systems while preventing unnecessary exposure of underlying personal or medical data.

Cleartext access is restricted to tightly controlled workflows where it is explicitly required.

Secure fraud detection and investigation pipelines

Fraud detection systems ingest detailed behavioral, financial, and claims data at scale. Insurance providers use encryption and tokenization to ensure sensitive fields remain protected throughout ingestion, analysis, and investigation.

Investigators can correlate activity and identify patterns without default access to full identities, reducing insider risk while preserving investigative effectiveness.

Reducing regulatory scope and audit complexity

By protecting sensitive fields before they reach downstream systems, insurers reduce the number of platforms subject to HIPAA, GDPR, and other privacy regulations. Analytics, reporting, and operational tools can operate on protected data without expanding audit scope.

This simplifies compliance while maintaining operational access to data.

Limiting insider access while preserving operational efficiency

Insurance organizations often have large internal user populations with legitimate system access. Rather than restricting system access entirely, sensitive values are protected so that users see encrypted, tokenized, or masked data unless explicitly authorized.

This reduces the impact of insider misuse and credential compromise without slowing day-to-day operations.

Secure data sharing across business units and time horizons

Insurance data is frequently shared across lines of business and retained for long periods. Tokenization enables consistent identifiers to be reused across systems and years while preventing unnecessary exposure of original sensitive values.

This supports long-term analytics, regulatory reporting, and operational continuity with strong data protection guarantees.

Common high-impact use cases in insurance

The following use cases are especially common in insurance. They tend to emerge as insurers manage decades-long data retention, complex claims ecosystems, and extensive third-party access, all while expanding analytics and AI initiatives.

Long-term protection of claims and policy data across decades

Insurance data often must be retained for decades to support claims resolution, litigation, regulatory review, and actuarial analysis. Customer identities, policy numbers, and claims histories are reused across multiple systems over long time horizons, increasing the risk of cumulative exposure.

Insurers address this by encrypting or tokenizing sensitive identifiers at the field level and using protected values consistently across systems and time. Tokens preserve referential integrity so historical data remains usable for analysis, audits, and re-opened claims, while original sensitive values are only accessible through tightly controlled workflows.

This allows insurers to meet long-term retention and audit requirements without repeatedly exposing sensitive personal or medical data as systems evolve.

Secure claims processing across third-party and partner ecosystems

Claims workflows frequently involve external adjusters, healthcare providers, repair services, legal partners, and reinsurers. These parties require access to claims data to perform their role, but do not require full visibility into all personal, financial, or medical details.

Instead of creating multiple masked copies or custom integrations, insurers protect sensitive fields directly and control cleartext access based on identity and role. Partners and internal teams work with tokenized or masked data by default, while cleartext access is limited to explicitly authorized functions.

This enables efficient claims collaboration across internal and external parties while reducing third-party exposure risk and simplifying compliance with privacy regulations.

Why traditional approaches fall short

Traditional data protection controls were designed for a different threat model than most organizations face today.

Storage-level encryption does not control data access
Techniques such as database transparent encryption (TDE), full disk encryption (FDE), and cloud server-side encryption (SSE) encrypt data on disk and in backups. They are effective against offline threats like stolen drives or backups. However, these controls automatically decrypt data for any authorized system, application, or user at query time. Once access is granted, there is no ability to restrict who can see sensitive values.

Encryption at rest is not an access control
Storage encryption is enforced by the database engine, operating system, or cloud service, not by user identity or role. As a result, there is no distinction between a legitimate application query and a malicious query executed by an insider or an attacker using stolen credentials. If a query is allowed, the data is returned in cleartext.

Sensitive data is exposed while in use
Modern applications, analytics platforms, and AI systems must load data into memory to operate. Storage-level encryption does not protect data while it is being queried, processed, joined, or analyzed. This is where most real-world data exposure occurs.

Perimeter IAM does not limit data visibility
IAM systems control who can access a system, not what data they can see once inside. After authentication, users and services often receive full visibility into sensitive fields, even when their role only requires partial access. This leads to widespread overexposure of sensitive data across operational, analytics, and support tools.

Static masking breaks analytics and reuse
Static or environment-based masking creates reduced-fidelity copies of data. This often breaks joins, analytics, AI workflows, and operational use cases, forcing teams to choose between security and usability. In practice, masking is frequently bypassed or inconsistently applied.

A false sense of security for modern threats
Most breaches today involve stolen credentials, compromised applications, misconfigurations, or insider misuse. Traditional controls may satisfy compliance requirements, but they do not meaningfully reduce exposure once data is accessed inside trusted systems.

As a result, sensitive data often remains broadly visible inside organizations, even when encryption and access controls are in place.

How organizations typically apply encryption, tokenization, and masking

In insurance environments, encryption, tokenization, and masking are typically applied at the data layer, close to where sensitive fields are stored and processed. The same protection is consistently enforced across operational systems, analytics platforms, and external data flows.

Access to cleartext or masked values is tied to identity and role rather than embedded in application logic. This allows security teams to enforce policy centrally while data and application teams continue to operate, integrate, and scale their platforms.

The result is an environment where sensitive insurance data remains broadly usable, but only selectively visible when there is a clear, authorized need.

Technical implementation examples

The examples below illustrate how organizations in this industry apply encryption, tokenization, and masking in real production environments. This section is intended for security architects and data platform teams.

Preserving referential integrity for decades-long claims and policy data

Problem
Insurance claims and policy data must be retained and reused for decades. Sensitive identifiers are repeatedly accessed as claims are reopened, audited, or analyzed, increasing cumulative exposure as systems change over time.

Data in scope
Policy number, claimant ID, national identifier, claim reference

Approach
Sensitive identifiers are tokenized at the field level and used consistently across systems and time. Tokens preserve referential integrity so historical records can be joined and analyzed without exposing original values. Cleartext access is restricted to tightly controlled claims and legal workflows.

Result
Supports long-term retention, audits, and analytics without repeatedly exposing sensitive personal or medical data.

Secure claims collaboration across third-party adjusters and partners

Problem
Claims workflows involve external adjusters, repair services, healthcare providers, legal partners, and reinsurers. These parties require access to claims data, but not full visibility into all sensitive fields.

Data in scope
Claimant identifiers, policy details, medical indicators, financial amounts

Approach
Sensitive fields are protected directly and partners operate on tokenized or masked data by default. Cleartext access is granted only to explicitly authorized roles and workflows, without creating multiple data copies or custom integrations.

Result
Enables efficient third-party claims processing while reducing partner exposure and simplifying compliance.

Tokenized analytics for actuarial modeling and pricing

Problem
Actuarial and pricing teams require large historical datasets to model risk and set premiums. Traditional masking often breaks joins and longitudinal analysis, leading teams to request access to cleartext data.

Data in scope
Policy identifiers, claim history, customer attributes

Approach
Identifiers are tokenized prior to ingestion into analytics platforms. Tokens allow joins, cohort analysis, and trend modeling while preventing exposure of real identities. Cleartext access is blocked in analytics environments.

Result
Enables advanced actuarial analysis and pricing models without expanding exposure of sensitive customer data.

Limiting insider access in customer service and operations systems

Problem
Customer service and operations teams require broad access to policy and claims systems, but do not need full visibility into sensitive personal or medical details.

Data in scope
Customer identifiers, policy numbers, partial medical information

Approach
Sensitive fields are dynamically masked or tokenized based on identity and role. Users see protected values by default, with cleartext access limited to approved escalation paths.

Result
Reduces insider risk and accidental exposure while preserving efficient customer and operations workflows.