Data Governance and Compliance on Mainframes: Aligning With Modern Regulations and Requirements

Craig Mullins explains how modernization efforts can help organizations get a handle on rapidly shifting compliance standards

For decades, the mainframe has been the trusted system of record for the world’s most critical applications and data. Its reputation for security, reliability and auditability has made it indispensable in industries like banking, healthcare, insurance and government. Yet today, compliance requirements are shifting rapidly. Regulations now extend beyond financial and healthcare mandates into areas such as data privacy, AI transparency and even environmental, social and governance reporting.

Organizations must therefore modernize their approach to data governance on the mainframe. The good news is that mainframes are uniquely positioned to meet these demands, but only if governance practices evolve in step with new regulations.

Why Data Governance Matters More Than Ever

At its core, data governance provides the framework for ensuring that enterprise data is available, secure, accurate, and usable.

Data governance is the set of policies, processes, and controls that ensure enterprise data is accurate, secure, consistent, and used responsibly across its lifecycle.

Historically, governance has focused on meeting regulatory requirements like SOX, HIPAA and PCI DSS. But the regulatory landscape has expanded. As AI has grown and been embraced ubiquitously throughout most organizations, AI governance demands that organizations be able to explain how machine learning models are trained, what data was used and whether bias has been introduced.

Additionally, more data privacy laws such as the EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) require organizations to honor data subject rights, including the right to access or erase personal data. This is sometimes referred to as the “right to be forgotten.”

Furthermore, environmental, social and governance (ESG) reporting obligations compel companies to capture and validate data related to sustainability, diversity and ethical practices.

These new regulations all share one common theme: traceability and accountability. Organizations must not only safeguard data but also be able to demonstrate lineage, usage and compliance at any point in time.

Mainframe Strengths in Governance and Compliance

The mainframe is hardly a newcomer to compliance. Its design inherently supports many of the requirements regulators now expect. For example, mainframes excel at:

• Transaction integrity and immutability – The mainframe’s robust logging and journaling systems create audit trails that are well suited for compliance validation.
• Proven security frameworks – RACF, ACF2, and Top Secret have provided fine-grained access control and authentication for decades.
• High availability and scalability – Essential for continuous compliance monitoring and uninterrupted regulatory reporting.
• Built-in encryption and masking – Capabilities within Db2 for z/OS and related environments strengthen the protection of sensitive data.

These attributes make the mainframe not an obstacle to modern compliance but an ideal platform for aligning governance practices with today’s regulatory expectations.

Challenges Facing Mainframe Data Governance

Even with these strengths, organizations face new challenges as they attempt to govern mainframe data in a modern context.

Perhaps the biggest challenge comes when integrating hybrid and multi-cloud into the mainframe environment. Such integration can complicate governance efforts by creating a decentralized and complex environment that challenges traditional oversight models. The primary issues stem from a lack of unified visibility, inconsistent security policies and complex compliance requirements across different platforms. Simply stated, once data moves beyond the mainframe, control weakens.

Another traditional, yet lingering concern is the existence of data silos and shadow IT, which can increase the risk of noncompliance by fragmenting governance across disconnected platforms. This can exacerbate the increasingly fragmented visibility and management that can arise when adopting a hybrid cloud approach.  With data and applications spread across multiple public cloud providers and on-premises infrastructure, it becomes difficult to get a complete, real-time picture of your IT environment. Each cloud has its own management tools, dashboards, and APIs, leading to a fragmented view. This makes it hard to track resource usage, monitor performance and enforce consistent policies. This lack of centralized visibility can also lead to shadow IT, where departments or individuals provision their own cloud services without corporate oversight, creating security and compliance risks.

Further complications are arising with AI adoption, which can raise significant questions and challenges when large language models (LLMs) are trained on or interact with mainframe data. Mainframes, while incredibly powerful and reliable, were developed in a different era with different data paradigms. This fundamental difference creates a series of questions that organizations must address. For example, mainframe data is often isolated in proprietary formats and is not easily accessible to modern, cloud-based AI platforms. It’s not a simple matter of “lift and shift.” Additional issues include historical bias in the data that may no longer be relevant, lack of contextual metadata and a potential skills gap as long-term mainframe professionals who understand the system and the data retire.

An often-neglected aspect of governance is understanding the data retention requirements of regulations. Many regulations dictate how long data must be retained, with different types of data having different retention requirements that may span from a few months to multiple years or even decades. Indeed, industry analysts have estimated that there are over 150 laws and regulations that mandate the duration of data retention. As such, organizations need to formulate plans for archiving data from the operational databases.

Of course, these are just a few of the biggest concerns. There are others, such as ESG reporting demands, data quality and integrity issues, batch processing schedules that can interfere with real-time data ingestion requirements, and more. In short, compliance on the mainframe is no longer limited to “locking down” data. It requires active management of data across its entire lifecycle, both on and off the platform.

Key Practices for Modern Compliance on Mainframes

To meet these challenges, organizations should adopt several governance practices tailored for today’s regulatory environment. First, a robust ability for managing metadata and tracking data lineage is essential. Comprehensive metadata catalogs allow organizations to demonstrate where data originates, how it changes and where it flows. This is critical not just for privacy audits but also for AI transparency and ESG reporting.

Privacy-by-design is another crucial practice for modern mainframe compliance initiatives. Sensitive fields should be encrypted, masked or tokenized. Access must be limited by role, and privacy considerations should be embedded into system design rather than applied retroactively.

Automation and continuous monitoring are also critical. Manual audits are no longer sufficient. Automated monitoring provides real-time detection of unauthorized access, policy violations or data misuse. Integrating these checks into DevOps pipelines helps ensure compliance from development through production.

Finally, it is important to ensure the consistency of your compliance frameworks across systems, whether data resides on the mainframe, in the cloud or across distributed systems. Federated governance solutions, such as with data fabric and data mesh, can help unify standards across platforms.

Tools and Technologies Supporting Mainframe Governance

Fortunately, a growing ecosystem of tools exists to help organizations align mainframe governance with modern compliance needs:

• IBM Guardium provides activity monitoring and compliance reporting across structured and unstructured data.
• IBM Data Privacy Passports extends security beyond the mainframe, controlling access as data moves across environments.
• IBM Watson Knowledge Catalog supports metadata management and AI governance, providing traceability and lineage.
• Db2 for z/OS features such as auditing, field-level encryption and row permissions and column masking add compliance-ready capabilities at the database level.
• Third-party solutions from vendors like Broadcom, Rocket and BMC complement IBM’s offerings with additional governance, monitoring and reporting functions.

By combining these tools with strong governance policies, organizations can build compliance frameworks that stand up to regulatory scrutiny.

New Challenges Signal the Mainframe’s Vitality

The compliance landscape has grown more complex, expanding from financial controls into areas like privacy, AI governance and ESG accountability. While these new requirements pose challenges, they also highlight the enduring value of the mainframe.

With its inherent strengths in security, reliability and auditability, the mainframe remains an ideal foundation for governance. But organizations cannot rely on legacy practices alone. By modernizing their governance frameworks, embracing automation and extending compliance practices across hybrid environments, they can ensure that mainframe data meets today’s stringent requirements.

In doing so, they not only stay compliant but also build greater trust, transparency and resilience for the future.

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