Data Masking Software: Is Your Data Leaking Before You Know It?

Are you sure there are no data breaches in your company? Many will answer in the affirmative. But here are a couple uncomfortable questions: what happens to the copies of the database that developers use for tests? What data are marketers analyzing in BI systems? And who in the company actually has access to sensitive information?

In 2024, IBM research showed that 80% of all leaks are not due to hackers, but to internal errors and failure to comply with security standards. And most often the “vulnerable link” are test environments, analytical reports and even just “forgotten” files with real user identifiers, add to that integrations with external services — all these are potential points of vulnerability.

How does it happen? Simple. Most companies think of their security as antivirus, firewall and database encryption. But no one really thinks about the fact that employees are constantly creating test copies of that data. This is where data masking software is needed — a tool that turns real information (records, content, fields, values, identifiers, logs, metadata, entries, variables) into useless character sets while preserving its structure.

Let’s take a look at why information masking is not just a useful feature, but one of the key technologies for business protection.

Where are the hidden risks? Data outside of productio

Thinking that securing a database in production automatically guarantees the security of all company information is a mistake that can cost you about $4.45 million dollars (according to IBM, this is the average cost per incident). In fact, data goes through many stages of processing before it gets to the final product, and vulnerabilities can occur at each of them, such as identifiers caching, backups, logging, outdated test bases.

But in PFLB’s expert experience, the biggest bottlenecks are the following:

• Test environments — this is where real data is often used for testing, but developers and QA people forget to protect it.
• Analytics systems — we’re talking about BI reports that are uploaded to cloud storage or transferred to third-party services — they usually contain sensitive information.
• AI training machine intelligence training samples can use customers’ personal records without their consent — this violates data protection laws such as GDPR.
• Integrations with partners via APIs in CRM, marketing platforms or logistics systems — these can lead to uncontrolled data dissemination when information is transferred

Here’s a simple scenario: a developer copies a database from production to test a new feature. Seems like nothing critical? But if that copy contains real card numbers, addresses, or medical records, it’s a leak. Now imagine that such copies are created monthly, sent to analysts, testers, third-party contractors….

The problem is that if a company doesn’t have a clear data masking policy, it effectively loses control over who and where sensitive information is used. A reason to wonder, isn’t it?

Why doesn’t encryption solve the problem?

Companies often think that encryption is a one-size-fits-all pill. Perhaps it is, but it has one critical flaw: the records is still real and, given the key, it can be decrypted. In other words, this means that if an employee or contractor gains access to encrypted data, they can recover it all too easily.

Data masking software works differently: it replaces real entries with fictitious ones, preserving only its structure and volume. As a result, testers, analysts and external contractors work with the same tables and reports, but without the risk of leakage.

Method	How it works	When to use	Impact on speed	Average implement cost	Regulatory acts
Encryption	The data remains original, but encrypted. Access is only possible with a key.	To protect the database and transfer data over networks.	Reduces data processing speed by 10-30% due to the need for decryption.	From $10,000 to $500,000 (depending on the scale of the system).	GDPR, CCPA, HIPAA, PCI DSS.
Masking	Real data is replaced with fake data, but its structure is preserved.	For testing, analytics, training AI, BI tools.	Minor performance impact (0-5%) as the data does not need to be converted back.	From $5,000 to $100,000 (depending on metadata volume).	GDPR, CCPA, SOX, ISO 27001.

Unfortunately, partners are not always scrupulous. If a leak occurs, encrypted data can be decrypted with the key, but masked data cannot.

How to choose data masking software that won’t create new problems?

Data protection shouldn’t interfere with business operations. If the implementation of a masking system slows down processes or complicates analytics, the company will simply start looking for workarounds — which means security will be jeopardized again.

According to the expert opinion of our colleagues at PFLB, there are a number of key factors that should be considered when choosing data protection software in order to avoid these problems:

• Compatibility with different data sources. If the tool works only with SQL databases, but does not support NoSQL stores, API requests or cloud infrastructure, there will still be vulnerabilities in the system.
• Minimal performance impact. Some solutions load databases so heavily that developers start disabling protection for the sake of speed. This is a compromise that can be too costly.
• Automation. If masking requires manual configuration every time a test environment is created, it will simply stop being used. Modern tools should integrate with CI/CD and work without constant intervention.
• Configuration flexibility. Different data requires different levels of protection: bank card numbers can be partially hidden, leaving only the last 4 digits, while medical records should be completely anonymized.

If a tool slows down testing, complicates analytics, or requires too much manual work, it’s a hindrance, not a help. A good solution is not only about security, but also about usability without sacrificing speed and efficiency.

Data masking is not only security, but also protection from legal risks

When one of the largest companies in the US allowed 50 million customers’ data to be leaked in 2022, their fine amounted to $700 million. In Europe, similar breaches result in fines of up to €20 million or 4% of annual turnover (CSO Online).

But even leaving the legal aspect aside, there’s another issue — user trust. After leaks, customers leave, and it is much harder, if not impossible, to get them back than simply buying a new data protection tool.

The question today is not whether data masking is needed, but when it will be implemented, and here’s a detailed study on ResearchGate that covers everything from concept to practical implementation.

Conclusion: What to do right now?

The first step is to figure out exactly where your company is using real data. Often, sensitive information ends up in the most unexpected places: developer test environments, analytic reports uploaded to cloud services, or third-party integrations with marketing and logistics platforms.

It’s important to check which of these entries really need to be protected and where they could become vulnerable. Simply restricting access is not enough — you need a strategy to secure information without slowing down processes. Implementing data-hiding software can help control leaks where they are not normally looked for and prevent sensitive records from being used unnecessarily.

Security isn’t just about protecting against cyberattacks, it’s also about controlling how data moves within a company. The sooner you start managing this process, the less chance you have of information falling into the wrong hands.

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