Data masking and data governance: Ensuring data integrity

You wouldn't ship code without version control or deploy without CI/CD. So why would you handle sensitive test data without masking? Most IT teams build pipelines with strong schemas and guardrails, but when it comes to using test data for development, data security often falls behind.

Data masking isn’t a checkbox. It’s a foundational safeguard against breaches, bugs, and bottlenecks. By replacing sensitive values with non-sensitive equivalents that preserve format and function, masking protects your systems without disrupting development. And when combined with governance—the policies and processes that ensure quality, security, and compliance—you gain something stronger: data integrity at scale.

Types of data that require masking

Data masking begins with understanding your exposure. The types of data most in need of protection are those that, if leaked, misused, or mishandled, introduce compliance violations, reputational damage, or direct harm to users. These categories require protection due to their sensitivity and potential for misuse:

• Personally identifiable information (PII): Names, addresses, social security numbers, and email addresses that can identify individuals. This data appears in virtually every application and creates massive liability when exposed in non-production environments.
  
• Protected health information (PHI): Medical records, treatment histories, and health-related data governed by HIPAA regulations. Healthcare applications require especially robust masking due to severe regulatory penalties and patient privacy concerns.
  
• Payment card information: Credit card numbers, CVV codes, and transaction data subject to PCI DSS compliance requirements. Financial applications must mask this data to prevent fraud and maintain payment processor relationships.
  
• Intellectual property (IP): Proprietary algorithms, trade secrets, and confidential business information that provides competitive advantage. Protecting IP in development environments prevents accidental exposure to unauthorized personnel.
  
• Sensitive business data: Customer lists, pricing strategies, and internal communications that could damage business relationships or competitive positioning if disclosed. This category often gets overlooked but represents significant business risk.

You also need to think about re-identification risk. Two anonymized fields might seem harmless until they’re combined. That’s why masking must consider field relationships, not just field contents.

Data masking in data governance frameworks

Data masking sits at the core of any data governance plan. Increasing investments in AI and ML systems accelerate the need for production-like data, but they also multiply the risks. In fact, the 2024 IBM Cost of a Data Breach Report shows breach costs have climbed to $4.88 million, a 10% year-over-year increase—the steepest since the pandemic.

Security and compliance expectations have evolved in kind. Regulations like GDPR and CCPA require visibility, traceability, and user-level control over data. You need to demonstrate lineage, limit access by role, and support deletion on request. Traditional security measures weren’t built for this.

In addition, cloud-native environments and microservices fragment your data landscape. Each container, API, or ephemeral instance adds another potential leak. AI pipelines, which often rely on large datasets to function, widen the attack surface further.

Data masking helps close this gap. It turns production data into safe, testable datasets that behave realistically without exposing sensitive content. 

Data masking for ISO 27001

ISO 27001 is becoming a go-to standard for mid-sized tech firms, especially those pursuing enterprise customers. Data masking is now explicitly required under ISO’s control set. To comply, you’ll need to:

• Document your masking strategy
• Audit usage regularly
• Monitor effectiveness through output checks

The goal is repeatability and resilience. If your masking logic breaks or coverage gaps appear, your framework should catch it before your auditor does.

Data masking best practices for data governance frameworks

You can’t retrofit governance. To scale securely, build masking into your workflows from the beginning. These practices can provide you both compliance and developer velocity. And they’re not just one-time setup tasks—they demand continuous attention, iteration, and alignment with your organization’s architecture and policies.

Why?

Many breaches happen not because masking was never set up, but because the policy got stale. Masking logic that worked six months ago may not cover new tables, new services, or new risks introduced by platform changes. Effective governance includes periodic reassessment and expansion of masking coverage.

Let’s look at how to make these data masking best practices real-world ready.

Determine your project scope

Start with discovery. Tools like Tonic Structural automatically scan schemas and flag potentially sensitive fields. The platform also detects schema changes and alerts you when new fields appear, helping ensure your masking coverage stays complete.

Then match users to access needs. Devs may need ZIP codes but not full addresses. QA teams might need consistent IDs but never real names. Tailoring access reduces unnecessary exposure and sets the foundation for effective masking.

Be specific about user roles, down to the data level. Overexposure often starts with overly broad defaults. Mapping those roles early prevents accidental sprawl, especially in distributed or cross-functional teams.

Evaluate your solution options

You could build your own masking scripts. But consider the full scope. Are you handling multi-table joins, role-based access, or complex compliance logic? If so, the DIY path becomes costly.

Commercial platforms like Tonic.ai offer:

• Deterministic masking
• Format-preserving encryption
• Synthetic data generation
• CI/CD integration
• Role-based controls and audit logging

These capabilities don’t just simplify setup; they reduce long-term risk and ops burden.

Also consider maintenance. A homegrown solution might work today, but can it evolve with your data architecture six months from now? Adding one new microservice or changing your database structure shouldn’t break your masking setup. A robust vendor solution can adapt as you scale.

Here’s how to think through the build vs buy decision.

Choose the right algorithms

Choosing the right algorithms depends on the data you’re using and the use case:

• SSNs and credit cards: Use format-preserving encryption to retain structure.
• Emails and usernames: Generate realistic names and randomized domains.
• Numeric values: Use bounded randomization to preserve distributions.
• Relational keys: Apply deterministic masking to preserve joins.

Statistical modeling and validation checks (e.g., Luhn checks, ZIP-state mapping) ensure your masked data behaves realistically without introducing noise that breaks downstream logic—which can contribute to extended breach lifecycles and inflated remediation costs.

You want output that’s accurate enough for ML, safe enough for compliance, and consistent enough for automated testing. Document the masking algorithms used for each data type so the results are traceable and auditable when policies or teams change.

Maintain referential integrity

Masking is only useful if it preserves test coverage. That means maintaining relationships across datasets.

Use deterministic masking so that masked user IDs remain consistent across logins, purchases, and sessions. Reflect real-world distributions with column linking—like geographic clustering or purchase volume—so behavior doesn’t flatten into randomness.

Also, audit edge cases. If your masked data includes invalid timestamps or misaligned dates, your QA and ML tests will suffer. Beyond that, validate masked datasets against expected business rules. This catch-all step can surface issues no schema scan will catch. And when these checks are automated, they become part of your safety net—not a manual bottleneck.

Operationalize masking end-to-end

Treat masking like infrastructure. That means:

• Automating refreshes
• Scanning for leaks
• Auditing config drift

Use infrastructure-as-code to define your rules. Set up alerting for failures. Fold masking into your CI/CD pipeline so no dev environment goes live without protection.

Go further by integrating test validations against masked datasets, especially in pre-production. You’ll catch data mismatches before they cause QA failures or delay releases. Also, consider building a small suite of validation tests that run after masking jobs complete. These can verify row counts, relational integrity, and value distribution within expected bounds.

Finally, don’t overlook documentation. Even if your tooling is automated, every team should know how masking decisions are made, which roles have access to what data, and how audits get triggered. Governance should make your organization more agile, not bogged down in guesswork.

Make it invisible. If your team doesn’t have to think about masking, you’re doing it right.

Where Tonic.ai fits in your governance playbook

Tonic.ai’s solutions are built to give developers access to safe, high-fidelity data without exposing real users or violating policy.

With the Tonic platform, you can:

• Discover and classify sensitive fields
• Apply masking algorithms tuned to your schema
• Preserve referential integrity and logic
• Automate data provisioning in CI/CD workflows
• Generate synthetic datasets for greenfield development or model training

Tonic Structural is the test data management platform for masking, subsetting, and transforming structured datasets. It includes the capabilities of Tonic Ephemeral, which lets developers spin up isolated masked databases for temporary environments—automated, consistent, and self-cleaning. This reduces manual environment management and removes the risk of data bleed across teams.

Tonic Textual extends masking and generation to unstructured data like freeform notes, documents, and logs. It supports formats like JSON, XML, and plain text, detecting embedded sensitive information and replacing it with realistic, policy-compliant substitutes.

Tonic Fabricate supports synthetic data use cases where no production data yet exists, helping teams generate statistically accurate datasets from scratch, along with mock APIs.

Everything is tracked, auditable, and designed for scale. That’s what makes the platform a governance asset, not just a dev tool.

Security doesn’t have to slow you down. When data masking is part of your infrastructure, your team moves faster—with less risk. Tonic.ai helps you get there. Connect with our team to learn how.