Mainframe Data Masking: Guide to Securing DB2, IMS, and VSAM Data

Mainframe data masking protects PII and other sensitive data in legacy systems for software testing, analytics, GenAI data grounding, and B2B data sharing. 

Introduction

Masking mainframe data is challenging because of complex cross-system dependencies, specific encoding and unique data formatting, and the need to preserve field structures and business rules. At the same time, mainframe data is constantly replicated into lower environments for software testing, analytics, B2B data sharing, and AI.

This creates significant privacy and compliance risk. If sensitive mainframe data is not masked correctly, and if masked values are not consistent across mainframe and non-mainframe systems, organizations face both operational failures and regulatory exposure.

This guide explains how to secure data stored in Db2, IMS, VSAM, and other mainframe sources without breaking referential integrity, and how to ensure that masked data stays consistent as it moves into modern cloud, analytics, and application environments. It also details how K2view delivers a complete solution for mainframe data masking at enterprise scale.

Key takeaways

• Mainframe data masking must address EBCDIC encoding, fixed-length records, packed decimals, and strict structural dependencies.  

• Masking must remain consistent across mainframe and non-mainframe systems so that applications, analytics, and AI models continue to work correctly.  

• Static masking is ideal for software testing, analytics, B2B sharing, and AI, while in-flight masking protects the data as it moves from production to downstream systems.  

• Automated discovery, governance, and compliance capabilities help organizations meet GDPR, HIPAA, PCI DSS, and other regulatory requirements.

• K2view delivers an entity-based approach that preserves referential integrity across all systems while masking data in flight, at scale.

Why is mainframe data masking required?

Mainframe data masking transforms sensitive values in legacy systems so that they remain usable but cannot expose PII, PHI, payment data, or other sensitive information. The goal is to allow realistic and compliant use of mainframe data in non-production testing environments, analytics platforms, AI pipelines, and B2B data transfers.

Why is mainframe data masking so hard?

Mainframe data masking is difficult for several reasons that stem from the unique nature of legacy systems and the way their data is used across the enterprise.

1. Legacy formats and rigid data structures

   Mainframe systems use storage structures that differ significantly from modern relational or cloud databases. These include:

        * Db2 relational tables  

        * IMS hierarchical files  

        * VSAM datasets  

        * Sequential and flat files

    

   Mainframe data masking is challenging because these systems often contain fixed-length records, packed decimal fields, and EBCDIC encoding. If masking changes field length, encoding, or value structure, dependent applications can break.  

2. Cross-system referential integrity

   Another critical challenge is referential integrity. A customer, policy, or loan may be stored in many mainframe files and tables, and also in modern, non-mainframe systems. Masked values must stay consistent across all environments to preserve functional, semantic, and analytical validity.

3. Dynamic regulations  

   Mainframe data often includes sensitive information governed by multiple regulations, such as CPRA, GDPR, DORA, HIPAA, and PCI DSS that are constantly evolving. Sensitive values must be masked in accordance with current and future data privacy regulations.

4. Mainframe data masking techniques

   Mainframe data structures require masking methods that preserve encoding, fixed-length formats, and business logic. Common techniques include:

How is masking applied across mainframe environments?

In addition to the techniques above, organizations also choose how and when masking is applied. These are not techniques, but operational approaches used for different data flows:

Static data masking
Permanently transforms sensitive data before it is delivered to lower environments such as QA, development, analytics platforms, AI pipelines, or partner systems.

In-flight data masking
A secure implementation of static masking that applies the permanent transformation during data movement. This prevents sensitive values from ever being stored in staging areas outside the production mainframe.

Dynamic data masking
Data is masked at query time based on user privileges, allowing authorized users to see real values while others see masked values. This typically involves a query interception layer that modifies query results on the fly based on role-based access controls.

K2view enterprise data masking for mainframes

K2view provides a complete enterprise data masking solution  that addresses the unique complexities of mainframe data and hybrid environments. It anonymizes sensitive data at scale for compliant software testing, analytics, B2B data sharing, and AI, while preserving structure, format, and cross-system consistency.

In-flight and contextual masking

K2view supports in-flight masking for moving mainframe production data into downstream environments. Sensitive data is masked during extraction, so it never appears unprotected outside the production mainframe.  
Contextual masking preserves the semantic meaning, format expectations, and business logic of the data so that masked values behave like real values in downstream systems.

Referential integrity across all systems

K2view uses patented entity-based data masking to maintain referential integrity across all datasets. The data for each business entity, such as a customer, loan, or work order, is masked as a single integrated unit, even when the entity’s information is stored across mainframe and non-mainframe systems.

Automated sensitive data discovery

K2view automates the discovery, classification, and cataloging of sensitive data by scanning metadata and content across all systems, including legacy mainframe files.

Extensive and flexible masking functions

The solution provides a rich library of built-in masking functions and enables no-code creation of custom functions for any data masking need.

Connectivity to every enterprise data source

K2view connects to and masks data from mainframe and modern systems, including SQL and NoSQL databases, packaged applications, flat files, and more.

Masking for unstructured documents

K2view Enterprise Data Masking anonymizes sensitive data in PDFs, images, text documents – consistently with the data in the structured data sources

Proven performance and industry recognition

K2view is recognized in Gartner research as a Data Integration Visionary , and praised for innovation, scalability, and support for both static and dynamic masking.

Use cases for mainframe data masking with K2view

Mainframe data is used across a wide range of operational and analytical use cases, and masking this data – consistently with non-mainframe data – is essential for regulatory compliance. K2view supports several high-value use cases, including:

• Delivering compliant and realistic test data to dev and test teams

• Preparing masked datasets for analytics and BI workloads

• Grounding AI models safely with privacy-protected data

• Sharing data with partners or vendors without exposing sensitive information

• Migrating data from mainframe to modern systems 

Start your mainframe data masking journey

Mainframe systems store some of the most heavily regulated and operationally sensitive data in the enterprise. Their legacy structures and formats make data masking difficult.

K2view solves these challenges with an entity-based approach that preserves referential integrity, supports static and in-flight masking, automates governance, connects to all enterprise data sources, and anonymizes structured and unstructured data at scale.

Experience K2view Enterprise Data Masking for yourself by taking the interactive product tour .