Data masking and data governance: Ensuring data integrity

Author
Chiara Colombi
September 3, 2025

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.
  • Protected health information (PHI): Medical records, treatment histories, and health-related data governed by HIPAA regulations.
  • Payment card information: Credit card numbers, CVV codes, and transaction data subject to PCI DSS compliance requirements.
  • Intellectual property (IP): Proprietary algorithms, trade secrets, and confidential business information.
  • Sensitive business data: Customer lists, pricing strategies, and internal communications.

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.

Security and compliance expectations have evolved in kind. Regulations like GDPR and CCPA require visibility, traceability, and user-level control over data.

In addition, cloud-native environments and microservices fragment your data landscape. Each container or API 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. 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. They demand continuous attention, iteration, and alignment with your organization’s architecture and policies.

Determine your project scope

Start with discovery. Tools like Tonic Structural automatically scan schemas and flag potentially sensitive fields. Match users to access needs.

Be specific about user roles, down to the data level. Overexposure often starts with overly broad defaults.

Evaluate your solution options

Consider the full scope of your masking needs. Are you handling complex compliance logic? If so, the DIY path becomes costly. Commercial platforms like Tonic.ai offer:

Choose the right algorithms

Choosing the right algorithms depends on the data:

  • SSNs and credit cards: Use format-preserving encryption.
  • Emails and usernames: Generate realistic names.
  • Numeric values: Use bounded randomization.
  • Relational keys: Apply deterministic masking.

Document the masking algorithms used for each data type to ensure traceability and auditability.

Maintain referential integrity

Masking is only useful if it preserves test coverage. Use deterministic masking so that masked user IDs remain consistent across datasets. Audit edge cases to ensure quality.

Operationalize masking end-to-end

Treat masking like infrastructure. Automate refreshes, scan for leaks, and audit config drift. Integrate test validations against masked datasets before production.

Where Tonic.ai fits in your governance playbook

With the Tonic platform, you can:

Tonic Structural is the test data management platform for masking, subsetting, and transforming structured datasets. Tonic.ai helps you get there. Connect with our team to learn how.