How to protect data privacy without sacrificing data utility

As data breach and cyberattack threats continue to grow, data privacy has become a serious concern for organisations. A concern that is well-justified, as cybersecurity breaches can be devastating.

The impacts of such breaches aren’t only limited to financial losses and reputational damage either. There are also other liabilities to consider. Under privacy regulations and standards such as GDPR, HIPAA, and ISO27001, organisations have an obligation and responsibility to protect sensitive data.

This has created a tension between the need for data utility and the importance of data privacy.

On one hand, having access to valuable data allows organisations to make informed decisions and drive innovation. But on the other hand, using this sensitive data presents a significant, potentially catastrophic risk. This has led to the widespread belief that data privacy and data utility are a trade-off – you can have one but not the other.

But this doesn’t have to be the case. With the development of data masking technology, organisations can strike a balance between data privacy and data utility. Data masking, also known as data obfuscation, is the process of replacing sensitive data with realistic and functional values while maintaining data usability and integrity.

This means that organisations can still have access to realistic and functional data without risking the exposure of sensitive information. Using a tool like DataMasque, organisations can integrate data masking into their data provisioning pipelines (the process of copying data from production to non-production environments). This allows for a carbon copy of production datasets, where sensitive data is replaced with values that look and act like the real thing, but don’t expose any sensitive information.

One of the main advantages of using data masking is that it allows for the creation of test and development environments that closely resemble real-world scenarios. Instead of creating custom data for testing, which may not accurately represent the behaviour of real customers, organisations can use masked data that closely mimics the behaviour of actual data. This not only speeds up the development process, but also ensures that the products and services being tested are more accurate and reliable.

The diagram below shows an example of how masked data can retain its utility:


Data masking can also be used for analytics and reporting. Often, sensitive data is required for analysis and reporting, but using the actual data poses a security risk. With data masking, organisations can use realistic and functional values for analytics without compromising data privacy.

A North American insurer with global operations needed to ensure they met both local and international data protection laws while building their Data Lake environment. By leveraging DataMasque, the Insurer was able to de-identify the data while maintaining the fidelity required for analysis before moving the data into their Data Lake.

Ultimately, there doesn’t have to be a trade-off between data privacy and data utility. Data masking can provide both.

With the right tools and processes in place, organisations can protect sensitive data while still having access to realistic and functional data for testing, development, and analytics.

By implementing a data masking tool like DataMasque, organisations can minimise the risk of data breaches and comply with privacy regulations, while still reaping the benefits of data-driven decision making.

To find out how data masking can protect your organisation’s data while preserving its utility, visit

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