This guide is essential for:
If you manage sensitive data on IBM z/OS mainframes and need to demonstrate compliance with modern regulations while embracing cloud analytics, this guide provides the roadmap.
Achieving robust mainframe security compliance in 2026 demands a data-centric, agentless approach that protects sensitive information before it leaves the mainframe. This strategy drastically reduces audit scope and enables Zero Trust principles without disrupting critical operations.
This modern approach shifts focus from securing perimeters to securing data itself. Even if systems are compromised, the underlying sensitive information remains valueless to attackers. By neutralizing data at the source, organizations can confidently leverage cloud services for analytics and modernization while maintaining the highest standards of security and regulatory adherence.
The modern approach to mainframe security focuses on rendering data valueless to attackers rather than relying solely on perimeter defenses.
The landscape of mainframe security compliance has rapidly evolved as critical systems integrate with hybrid cloud environments. Your data perimeter has expanded significantly, subjecting mainframe-origin data to new regulatory pressures and security complexities that didn't exist five years ago.
Mainframes running IBM z/OS remain the backbone for core enterprise functions. These systems process immense volumes of sensitive financial, health, and personal data. Your business depends on these systems for high-volume transaction processing – often 50,000 transactions per second or more – making data protection a top organizational priority.
The shift toward hybrid architectures is essential for digital transformation initiatives like big data analytics and machine learning. This transformation has fundamentally changed mainframes from isolated systems into integrated components of a broader IT ecosystem.
As sensitive data flows through complex data pipelines to cloud data lakes and SaaS applications, it introduces new attack vectors. Legacy security practices cannot adequately address these modern threats, as detailed in a 2023 Help Net Security analysis. This expanded data footprint requires a modern security approach that extends beyond the traditional mainframe environment.
Mainframe security is strong, but data must be continuously protected after leaving z/OS.
Global regulatory scrutiny has intensified significantly in 2026. Standards like the Payment Card Industry Data Security Standard (PCI DSS) version 4.0, the Health Insurance Portability and Accountability Act (HIPAA), and the General Data Protection Regulation (GDPR) impose strict mandates on data protection.
Compliance now requires demonstrating consistent control over mainframe-origin data throughout its entire lifecycle:
Severe penalties for non-compliance have elevated data protection from an IT concern to a critical business function. Organizations face potential fines reaching 4% of global annual revenue under GDPR, or millions in PCI DSS penalties.
In this new reality, traditional perimeter-based security and legacy mainframe tools are insufficient. A firewall protecting your mainframe offers no security once data has been extracted and sent to a cloud environment.
To address this gap, you must adopt an 'assume breach' mentality. This principle, central to frameworks from the National Institute of Standards and Technology (NIST), posits that perimeter defenses will eventually be bypassed. Modern attackers are sophisticated, patient, and well-resourced.
Therefore, the primary objective must be protecting the data itself. By rendering data unintelligible to unauthorized parties, you ensure that even if a system compromise occurs, the core asset remains secure. This is the foundation of modern mainframe security in 2026.
Traditional mainframe security models – Resource Access Control Facility (RACF), ACF2, and Top Secret – are highly effective at managing permissions within z/OS. However, they cannot extend consistent controls once data leaves your mainframe.
These tools excel at defining access on the mainframe, but their authority ends at the system's edge. When data is extracted via an Extract, Transform, Load (ETL) process and sent to a cloud data warehouse, the granular policies defined in RACF become irrelevant.
Mainframe security policies do not extend to AWS, Azure, or Google Cloud. Each downstream system requires a separate security implementation.
The 'blast radius' of a potential data breach expands exponentially when unmasked sensitive information flows from a mainframe into a distributed network of cloud applications and analytics platforms.
If raw Primary Account Numbers (PANs) propagate across this ecosystem, the scope of your compliance audit under PCI DSS must encompass every system that handles them. This dramatically increases:
A failure in any downstream system can lead to a catastrophic breach of your mainframe data, regardless of z/OS security strength.
Agent-based security solutions require installing software directly on the mainframe. This introduces significant operational overhead and risk that organizations cannot accept in 2026.
Mainframe environments are engineered for high performance and stability. Installing third-party agents:
An unstable agent can crash a mission-critical system processing billions in transactions. This makes an agentless approach far more desirable for maintaining the integrity of core mainframe workloads.
Legacy security tools native to the mainframe also lack the field-level granularity required for modern data-centric controls.
While RACF can control access to an entire file or dataset, it cannot easily enforce a policy that dynamically masks a specific Social Security Number field for a developer while revealing it to a customer service representative. This inability to apply context-aware, field-level controls makes it difficult to implement the principle of least privilege.
The result is an increased risk of both accidental exposure and insider threats. Developers shouldn't see production credit card numbers during testing, but traditional mainframe security makes granular masking extremely difficult to implement.
Relying on separate security stacks for your mainframe and the cloud creates a disjointed security posture with dangerous gaps.
Your mainframe security team manages RACF policies, while your cloud team manages cloud-native Identity and Access Management (IAM) policies. This separation leads to:
A unified data protection strategy that transcends environmental boundaries is an absolute necessity for businesses in 2026. Attackers do not respect organizational silos, and security architectures should not create them either.
Embracing Zero Trust for mainframe data involves applying a "never trust, always verify" security model to every access request. This protects sensitive information as it moves from the inherently secure mainframe to hybrid and cloud environments.
This model, detailed in NIST Special Publication 800-207, assumes the network is always hostile. Every user, device, and application must prove its identity and authorization before being granted access.
For mainframe data, this means:
Zero Trust recognizes that breaches are inevitable. The question isn't if attackers will penetrate your network, but when. Your security must assume compromise and protect data accordingly.
This approach marks a critical shift from network-centric security to a data-centric strategy. The data itself becomes the primary focus of protection efforts.
The goal is to make data inherently secure so its protection persists across all environments. Security controls are attached to the data element itself, not the database, application, or network segment.
According to NIST guidance on Data-Centric Security, this ensures that even if an attacker breaches your network defenses, stolen data is rendered useless. The strategy neutralizes the value of stolen data rather than solely attempting to prevent theft.
This data-centric approach fundamentally changes how organizations protect mainframe data in hybrid environments.
The Four Pillars of Data-Centric Mainframe Security
The foundation of a successful data-centric or Zero Trust strategy is knowing where your sensitive data lives.
This process involves systematically scanning mainframe datasets—Virtual Storage Access Method (VSAM) files, Database 2 (DB2) tables, and sequential files—to identify where sensitive data elements reside:
Once discovered, this data is classified based on sensitivity level. Classification automatically applies appropriate Zero Trust protection policies, enabling security at scale.
Discovery is the critical first step in any data protection strategy.
With sensitive data identified, your next step is to enforce granular, data-centric policies at the field level. This uses technologies like tokenization and dynamic data masking.
Example policies include:
This enforcement of least privilege ensures applications and users only access the minimal data necessary. This drastically reduces the risk of exposure—both from external attacks and insider threats.
Modern data-centric security makes access decisions based on:
A customer service representative accessing data from a corporate laptop during business hours receives different data than a developer accessing the same record from a home network at 2 AM.
A robust Zero Trust posture depends on continuous monitoring and auditing of all data access and usage.
Every request to access sensitive data and every policy enforcement action must be logged and analyzed. By integrating these event streams with your Security Information and Event Management (SIEM) systems, you can:
This completes the cycle of continuous verification that defines Zero Trust. Implementing this data-centric model effectively requires a technical approach that applies these protections without disrupting core systems.
Agentless architecture is the foundational technical strategy for implementing Zero Trust principles discussed previously. This approach enables you to apply robust data protection without installing software on z/OS.
Instead of placing a resource-intensive agent on your mainframe, an agentless solution functions as a network gateway or proxy. The architecture works as follows:
All of this happens transparently to both source and destination systems. Neither your mainframe applications nor your cloud services need modification.
This network-level approach eliminates the operational overhead and risk of mainframe agents while delivering enterprise-grade data security.
A primary benefit of this architecture is its ability to deliver powerful data security with "no code changes" – a critical requirement for legacy mainframe applications.
Modifying decades-old COBOL code is risky and expensive:
An agentless solution operates transparently to the source and destination systems. This approach reduces risk in your security projects, accelerates time-to-value, and preserves the stability of business-critical mainframe operations.
Organizations can implement enterprise data security without modifying COBOL applications. This approach significantly reduces risk for organizations with decades-old applications that have limited documentation or available expertise.
Within an agentless framework, it's crucial to distinguish between tokenization and encryption. Both protect data, but they have very different implications for compliance:
This process replaces a sensitive data element with a non-sensitive substitute called a token. The original data is securely stored in a centralized vault, separate from your operational systems.
Key characteristics:
Because the token is not sensitive data, downstream systems that store it are often removed from the scope of compliance audits, as confirmed by PCI Security Standards Council guidelines. While we introduce the concept here, the next section details how this directly reduces your compliance audit scope.
This method uses a cryptographic algorithm to transform data into an unreadable format. It's effective for protecting data at rest and in transit.
Key characteristics:
Because encrypted data can be reversed to its original form with the correct key, systems storing it often remain within your scope of compliance audits. The data is still "there"—just in a different form.
By deploying these agentless technologies, you effectively neutralize the value of your sensitive mainframe data to attackers.
If a downstream cloud database is breached, attackers will only find:
The actual sensitive data remains safely isolated in a secure vault, protected by enterprise-grade security controls and HSMs. This "data neutralization" is the ultimate goal of a data-centric security model.
For an in-depth look at this process, explore our guide on Mainframe to Cloud: Secure Solutions for Data Pipelines.
Tokenization serves as your most powerful tool to directly and dramatically reduce the scope of PCI DSS audits. Organizations regularly achieve scope reductions of 80-90% using this approach.
The PCI DSS framework applies to any of your system components that:
By using tokenization to replace the sensitive Primary Account Number (PAN) with a non-sensitive token as data leaves the mainframe, all downstream systems no longer handle actual cardholder data.
Your data warehouse, analytics platforms, cloud applications, and reporting systems are effectively removed from PCI DSS audit scope. They're storing tokens, not real credit card numbers.
PCI SSC guidance explicitly supports this approach to tokenization and scope reduction.
This scope reduction translates into significant benefits for your business:
Reduced Audit Costs:
Lower Ongoing Compliance Burden:
Operational Efficiency:
As explained in guidance on reducing PCI DSS scope with tokenization, this not only simplifies your annual audit process but also reduces the ongoing operational burden on your security and IT teams.
The same principles apply directly to achieving HIPAA compliance for healthcare organizations.
By tokenizing or consistently masking Protected Health Information (PHI), you significantly mitigate the risk of a PHI breach:
This proactive de-identification of data helps you meet the technical safeguard requirements of the HIPAA Security Rule. The regulation mandates measures to protect against unauthorized access to electronic PHI during transmission and storage.
Tokenized data is rendered useless for identification purposes. Systems storing only tokens have significantly reduced HIPAA obligations.
From a global privacy perspective, tokenization is a key enabler for your GDPR compliance in 2026.
It directly supports the regulation's core principles:
Data Minimization: Process only the minimum personal data necessary for your purpose. By replacing personal data with tokens, you minimize the amount of identifiable information processed in non-essential systems.
Privacy by Design: Build data protection into systems from the ground up, not as an afterthought. Tokenization embodies this principle by making data protection intrinsic to your data flows.
Pseudonymization: Transform personal data so it can't be attributed to a specific individual without additional information. This act of pseudonymization is explicitly encouraged by GDPR Article 25 as an appropriate measure to protect data subject rights.
Organizations using tokenization can often process data for analytics and business intelligence purposes without the full burden of GDPR restrictions—because they're analyzing tokens, not actual personal data.
When it comes time for an audit, a modern data security platform generates comprehensive auditable evidence to prove your controls are effective:
Tokenization Logs:
Policy Enforcement Reports:
Key Management Records:
Data Lineage Documentation:
This documentation provides your auditors with concrete proof that sensitive data has been systematically protected throughout its lifecycle.
You can learn more by exploring On-premise & Hybrid Data Security Platform Alternatives.
Understanding the differences between modern agentless approaches and traditional security models is critical for making informed decisions in 2026.
Choose DataStealth When You Need:
✓ No mainframe performance impact for high-volume transaction processing (10,000+ TPS)
✓ Rapid deployment without extensive mainframe change management processes
✓ Legacy application support for 20+ year old COBOL applications without source code
✓ Massive scope reduction for PCI DSS, HIPAA, or GDPR compliance audits
✓ Hybrid cloud security protecting data from z/OS to AWS, Azure, or Google Cloud
✓ Zero code changes requirement to preserve application stability
✓ Field-level granularity with context-aware masking and tokenization policies
Consider Alternatives If:
The critical pillars for building secure mainframe-to-cloud data pipelines in 2026 are robust transport-level encryption, strict key separation, and inline data masking or tokenization.
All your data in transit must be protected using strong cryptographic protocols:
Unencrypted mainframe data flowing to the cloud is unacceptable in 2026—regardless of whether it's your private network. Attackers can intercept traffic at any point in the data path.
Your cryptographic keys must be managed separately from cloud provider keys to maintain control:
Never allow cloud providers to hold master encryption keys for your most sensitive data. You lose control and may violate regulatory requirements around key custody.
Most importantly, your sensitive data must be de-risked with inline tokenization or masking before it enters the pipeline:
This is the difference between protecting data in transit and neutralizing the data itself. Even if encryption fails, tokenized data has no value.
Integrating agentless tokenization into modern data pipelines, such as those built on Apache Kafka, is a seamless process in 2026.
A data security gateway can be configured to act as a transparent proxy:
Neither the producer nor the consumer needs modification. The gateway operates as an invisible security layer in your data pipeline.
This same pattern works for:
For traditional ETL processes extracting data from DB2 or VSAM:
DB2 Extract → Security Gateway → Token Vault → Cloud Data Warehouse
↓ ↓ ↓ ↓
Raw PANs Intercept & Scan Store Original Receive Tokens
Apply Policies Issue Token Analyze Safely
The gateway intercepts the data stream between source and destination, applies policies, and ensures only protected data reaches the cloud.
Proper cryptographic key management is non-negotiable in 2026. Your data security platform must integrate with enterprise-grade infrastructure:
Hardware Security Modules provide the highest level of key protection:
Enterprise KMS provides centralized key lifecycle management:
Critical: The custody of master keys should always remain with you, not outsourced to third parties. This provides a critical layer of control and meets regulatory requirements around key ownership.
For continuous security monitoring, your data security platform must be tightly integrated with your SIEM systems.
All significant events should be forwarded to the SIEM in real-time:
This allows your Security Operations Center (SOC) to:
For Site Reliability Engineers (SREs) and Cloud Architects tasked with securing these pipelines, follow this structured approach:
Begin by mapping data flows to identify all paths sensitive data takes:
Document every system that touches sensitive data from mainframe to consumption.
Pinpoint logical "choke points" where an agentless gateway can be inserted:
Fewer choke points mean simpler architecture and easier troubleshooting.
Plan for high availability and failover:
Your security layer cannot become a single point of failure for business-critical data pipelines.
Leverage infrastructure-as-code tools to automate deployment and configuration:
Ensure security policies are applied consistently across all environments (dev, test, prod) through automation, not manual processes.
For a comprehensive overview, see our Mainframe Security Solutions - Complete Guide.
Using live production data from your mainframes in development and testing environments introduces severe security and compliance risks that organizations can no longer afford in 2026.
Non-production environments typically have:
This creates a large and attractive attack surface. Exposing raw sensitive data in these areas creates an easy backdoor to your organization's most valuable information.
Recent breaches have repeatedly shown that attackers target development environments because they know production data lives there with minimal security.
The presence of unmasked sensitive data in non-production environments dramatically expands your scope of compliance audits.
If production cardholder data is copied to a test environment, that entire environment falls under full PCI DSS scope:
This creates an enormous compliance burden for systems designed for rapid iteration, not security lockdown.
Similarly, if production PHI appears in non-production environments:
Many organizations don't realize their entire development infrastructure is non-compliant until an audit reveals the issue.
An agentless data protection approach offers the solution by enabling masking or tokenization of live feeds as they're provisioned for non-production use.
A security platform intercepts a data replication stream destined for a test environment:
This provides development and QA teams with structurally and referentially intact data that behaves like production data—without any of the associated risk.
For Security Teams:
For Development Teams:
This method allows for the creation of safe, high-fidelity datasets perfect for development and testing.
Instead of using real credit card numbers, teams work with format-preserving tokens:
As noted by payment security leaders like Bluefin on tokenization, tokenized credit card numbers will still pass application validation—ensuring that application logic can be thoroughly tested without exposing sensitive information.
For other sensitive fields, generate realistic synthetic data:
The data looks real, behaves realistically in applications, but contains zero actual sensitive information.
Integrating this process into modern Development, Security, and Operations (DevSecOps) pipelines is critical for maintaining developer velocity.
The provisioning of masked data can be fully automated:
Production DB → Automated Extract → Security Gateway → Masked Test DB
(nightly schedule) (auto-masking) (auto-refresh)
Developers wake up each morning to refreshed test data that's:
Security gates in your CI/CD pipelines ensure:
This approach enables security integration earlier in the development lifecycle while maintaining developer productivity.
Proving your compliance with data residency and cross-border data flow regulations for mainframe data is achieved by implementing technical controls like regional tokenization. This keeps sensitive information within its country of origin while allowing non-sensitive tokens to be used for global analytics.
Navigating the intricate web of global data privacy laws presents a significant challenge for multinational organizations in 2026:
These regulations often impose strict data residency requirements, mandating that personal data of citizens remain within their region. Violating these rules can result in:
Regional tokenization provides a powerful technical solution to meet data residency requirements while enabling global business operations.
This architecture involves deploying tokenization services or vaults within specific geographic regions:
Europe:
EU Mainframe Data → EU Token Vault (Frankfurt) → Generate Token
↓
Token (non-sensitive)
↓
Global Data Warehouse
(tokens from all regions)
Asia-Pacific:
APAC Mainframe Data → APAC Token Vault (Singapore) → Generate Token
↓
Token (non-sensitive)
↓
Global Data Warehouse
Americas:
US Mainframe Data → US Token Vault (Virginia) → Generate Token
↓
Token (non-sensitive)
↓
Global Data Warehouse
Your original sensitive data never crosses geographic boundaries. Only non-sensitive tokens are transmitted internationally.
This approach enables you to unlock the business value of global analytics on tokenized datasets while strictly adhering to data residency rules.
Analysts can:
All of this happens without ever accessing the raw, regulated personal data from other jurisdictions. You're analyzing tokens that have no intrinsic value or personal information.
This approach provides strategic advantages:
To prove compliance to regulators, you must be able to produce clear audit artifacts demonstrating that data never crossed borders improperly.
A data security platform generates detailed documentation:
Data Lineage Reports:
Tokenization Logs:
Geographic Boundary Enforcement:
Key Management Attestations:
These reports, supplemented by immutable tokenization logs, provide an auditable trail demonstrating that residency-aware controls were consistently applied.
This model has been validated by successful DataStealth case studies with multinational financial institutions and healthcare organizations operating across dozens of countries.
Global Bank Scenario:
Challenge: Global bank with mainframes in US, UK, Germany, and Singapore needs to:
Solution:
Result:
Regional tokenization enables multinational organizations to comply with diverse data residency requirements while maintaining global analytics capabilities.
Choosing the right mainframe data security platform is a critical decision that will impact your organization for years. Use these criteria to evaluate vendors.
Why This Matters: A solution requiring agent installation on z/OS or modifying legacy application code introduces unacceptable risks:
What to Require:
Questions to Ask Vendors:
An agentless platform that operates at the network layer provides robust security transparently, preserving the integrity of critical systems.
Why This Matters: The primary business outcome should be the platform's verified ability to significantly reduce compliance audit scope. Vendors should provide concrete evidence, not just claims.
What to Require:
Questions to Ask Vendors:
Independent validation proves the platform meets the highest industry standards and will effectively remove downstream systems from your audit scope.
Why This Matters: The platform must integrate with your existing enterprise security ecosystem. Isolated point solutions create security gaps and operational complexity.
What to Require:
Identity and Access Management (IAM):
SIEM Integration:
Key Management:
DevOps and Automation:
Questions to Ask Vendors:
Integration ensures consistent policy enforcement and unified visibility across your security stack.
Why This Matters: Different data types and use cases require different protection techniques. A platform with only one approach (e.g., only encryption) cannot meet all requirements.
What to Require:
Tokenization Options:
Masking Techniques:
Data Type Support:
Environment Coverage:
Questions to Ask Vendors:
The solution should support a wide variety of data types with both format-preserving and reversible tokenization options, plus a rich library of masking techniques.
Why This Matters: Your mainframes process thousands of transactions per second. Any inline security solution must keep pace without becoming a bottleneck or affecting application response times.
What to Require:
Performance Metrics:
Load Handling:
What to Request:
Proof of Concept (PoC) Requirements: Conduct a rigorous proof-of-concept test:
Questions to Ask Vendors:
Never deploy based on vendor claims alone. Insist on realistic PoC testing that proves the platform can handle your workloads without impacting business operations.
Why This Matters: Mainframe security is a long-term commitment. You need a vendor who will be around for the next decade, with ongoing product investment and support.
What to Evaluate:
Company Stability:
Product Maturity:
Customer Support:
Community and Ecosystem:
Questions to Ask Vendors:
The most important takeaway for modern mainframe security is the critical shift from a perimeter-based mindset to a data-centric security model. In a hybrid world, the only effective long-term strategy is focusing on protecting the data itself.
This data-centric vision is made practical and achievable through the adoption of agentless data protection, tokenization, and Zero Trust principles:
By embracing a modern data security platform in 2025, you achieve multiple strategic outcomes:
Modern data security platforms convert security from a cost center into a strategic enabler of digital transformation. Proactive adoption of advanced data protection strategies is essential to securing mainframe assets in hybrid environments.
Organizations must decide whether to lead or follow in mainframe security modernization.
Modernizing your mainframe security and achieving compliance in a hybrid cloud world provides both defensive and strategic advantages.
By adopting a data-centric, agentless approach, you can:
Take the next step to future-proof your data protection strategy.
See how DataStealth can help you secure your sensitive data. Schedule a demo today.
