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Data breaches expose sensitive customer and employee data, damaging trust and compliance. Much of this leakage occurs in lower-security test environments. Test data management (TDM) solutions mitigate this risk.

This guide explains how to use TDM. To better protect confidential information across testing and development systems. Follow these steps to reduce breach risks from test data sprawl.

Dangers of Unmanaged Test Data Environments

Common testing practices create data security gaps:

No oversight:  With no controls, testers take copies of production data without trackability.

Easy exposure: Test environments often lack security layers like production. This puts data at risk.

Data lingering: Test data remains unmasked and unmasked across systems, expanding exposure over time.

Unstructured processes: Ad hoc data handling without governance breeds non-compliance.

Sprawl: Uncontrolled test environment proliferation spreads data copies to more systems, increasing vulnerability.

These behaviors cause sensitive data to increase across low-security test systems. This heightens breach risks.

How TDM Protects Sensitive Data

Test data management solutions secure data by:

Test data management helps keep our important information safe. It puts on a disguise like a mask so nobody can see things like our email or social security numbers. It’s like wearing a costume to hide your real identity!

Test data management strategy creates pretend information that looks real but doesn’t belong to real people. For example, making up stories in a book, but these stories look like real life!

Instead of making many copies, like paper photocopies, test data management helps people use data from one place. Having a big library where everyone can read the same book without needing their copy sorted.

Think of test data management as a library that tracks who checks out a book and when they do it. This way, they know who used the data and when they used it. It’s like having a record of everyone who borrowed a book.

Imagine your computer deleting old files you don’t need anymore. Test data management does something similar. It gets rid of old data when it’s unnecessary to ensure it doesn’t stick around where it shouldn’t.

Step 1 – Identify High-Risk Data for Masking

First, inventory data types require masking. Note formats, uses, and locations. Methods include:

• Scanning environments to find sensitive data fields like credit cards, emails, and IDs.
• Classifying data by levels of confidentiality based on regulations and impact of exposure.
• Documenting data flows into test systems to reveal. What data gets copied from production?
• Having business leaders help identify priority fields for masking aligned to risks.

Understanding sensitive data to rank masking efforts is key to reducing exposure.

Step 2 – Select Optimal Masking Approaches

With priority data mapped, determine appropriate masking techniques:

Substitution: Replace real data with fictional but realistic values.

• Ex. Mask bob@company.com to john@company.com

Shuffling: Mix up data by switching parts but keep formats.

• Ex. Change Alice Smith to Sara Jones

Number variance: Alter numbers little within a small range.

• Ex. Change $72,000 salary to $71,592

Encryption:Encode data into an unreadable format without disrupting workflows.

Deletion: Ever remove certain high-risk data.

• Ex. Deleting entire credit card numbers.

Format preservation: Keep original formats without exposing real data.

• Ex. Format SSN 456-45-6789 to look like 999-99-9999

Combine approaches based on risk levels and intended uses.

Step 3 – Integrate Masking into Test Data Workflows

Next, build automated masking into flows:

Mask data at source: Before allowing copying to other environments. Mask sensitive fields in the production database or backups.

Configure access rules: Give teams access to original or masked data sets depending on the need to know and the environment.

Mask on copy: When users request test data copies. Configure rules to mask certain fields on provisioning.

Schedule recertification: Need teams to revalidate masking formats in case requirements change.

Track usage: Use audit logs to ensure proper data handling by users.

Masking should be integrated into test data practices without disrupting activities.

Step 4 – Expand Masking Across the Data Landscape

Once implemented, expand masking to other systems:

• Non-production databases: Mask data in dev, QA and UAT systems based on environment risks.
• Analytics: Mask inputs to models containing sensitive fields before analysis.
• Reporting: Redact confidential data from test reports before distribution to wider audiences.
• External sharing: Mask any data shared, like with offshored testing teams.
• New applications: Incorporate masking requirements into onboarding procedures for new systems.

Step 5 – Provide Ongoing Masking Management

Central govern masking for sustainability:

• Merge tools: Use solutions that provide enterprise-wide masking visibility and control.
• Automate compliance reporting: Leverage features that produce on-demand reports showing masking status.
• Track dashboards: Use dashboards to view masking volumes, performance, and audit logs.
• Update rules: Adjust recipes based on changing sensitivities and masking best practices.
• Assign responsibilities: Appoint specific teams to manage masking alongside other data security domains.
• Train testers on risks: Educate teams on the importance of masking for gaining buy-in.

Enterprise-grade TDM solutions like Delphix provides the advanced capabilities required. To integrate masking into your environment for comprehensive protection.

Integrate Masking Early Into New Application Development

Building in data masking during the software development life cycle for new applications is vital. Trying to retrofit masking after launch is difficult.

Define masking requirements alongside other security and compliance needs when scoping new apps. Document which data fields need masking or synthetic data generation. Then, work those capabilities into design discussions and coding sprints. Leverage tools with APIs that developers can integrate.

Getting masking engineered into applications from the start results in more secure software. Data is protected by default rather than through tacked-on controls. This prevents sensitive data exposure down the line when testing ramps up.

Expand Masking Beyond Testing into Other Areas

While testing is a priority, don’t limit data masking to those environments. Look to expand masking more broadly:

• Mask data during migration between systems to prevent exposure in transit.
• Build masking into ETL processes feeding downstream analytics systems to protect privacy.
• Mask reports containing sensitive information before being distributed to wide audiences.
• Mask data is shared between teams and regions to limit access.
• Mask data entered into employee training systems to preserve confidentiality.
• Mask data recovered from legacy systems is no longer needed to reduce retention risks.

Taking an enterprise view enables masking in diverse scenarios beyond testing. This provides defense-in-depth for your sensitive data assets.

Reduce Your Data Breach Risks

To stop your secrets from getting out, you need strict rules to protect them when doing tests. Using technology can help keep your secrets safe and ensure you have the data you need for tests. We can talk about how this tech can protect your important information.”