DPA/GDPR and data masking: securing sensitive data in 2024

The cost of a data breach can be substantial — both financially and reputationally.??

Since the introduction of the EU's General Data Protection Regulation (GDPR) in 2018, securing personal and private data has become a legal mandate for companies operating within the European Union.??

The Data Protection Act 2018 is the UK's implementation of the General Data Protection Regulation (GDPR). The regulations remain fundamentally similar, with stringent guidelines governing how businesses must handle personally identifiable information (PII), such as names, email addresses and other sensitive data.?

Non-compliance with these regulations can result in severe financial penalties. However, the consequences of a data breach extend beyond fines — trust and reputation are also at stake.??

The widespread adoption of remote and hybrid work following the pandemic means businesses face increased vulnerability to cyber-attacks — largely due to the extensive use of cloud-based tools without always having adequate security measures in place. This migration to the cloud heightens the risk of data breaches as sensitive information is stored and transmitted over the internet, making it susceptible to interception.?

This is why IT professionals are increasingly turning to advanced information security methods, including data masking, to protect sensitive data.?

What is data masking??

Data masking, also known as data obfuscation, is an automated process that replaces sensitive data with altered or dummy data whilst maintaining the dataset's usability. This ensures that while the data remains functional for testing or analysis, the sensitive information it represents is disguised, thereby reducing the risk of exposure during development or when shared with unauthorised third parties.?

Implementing data masking as a proactive measure ensures that even if a breach occurs, the masked data remains useless to attackers, thereby maintaining both data security and functionality.?

In organisations where employees have varying levels of access to privileged information, data masking can prevent internal data breaches by restricting access to sensitive data. Masking a data field ensures that only authorised personnel can view its contents, safeguarding PII and commercially sensitive information when sharing data internally or with third parties.?

Equally, software developers are increasingly adopting Agile methodologies and embracing DevSecOps (development, security, and operations) to ensure secure software delivery. During the software development and testing phases, data often needs to be shared, sometimes with third parties. To safeguard this data and maintain GDPR compliance, data masking plays a crucial role in protecting sensitive information from being exposed during these exchanges.?

Before I get onto the different data masking methods, it is also important to note that data masking is distinct from encryption.??

Encryption converts original data into an alternative form (ciphertext), which must be decrypted to be useful — introducing a vulnerability during the decryption process. Masked data, however, remains usable in its obfuscated state. The key question is not whether to mask or encrypt data but rather when to apply each method — or both — to optimise security.?

Which data masking method is right for you??

There are several data masking methods available, each suited to different business needs depending on the data's application and end use.?

Static data masking creates a masked version of a data set in its original environment, which is then copied to a separate database proxy for safe sharing. Some methods include the following:?

Dynamic data masking?

This method keeps sensitive data in its original database whilst shuffling its contents in real time to disguise them. Unlike static masking, it does not require a second data source, allowing authorised systems to access the masked data directly.?

On-the-fly data masking?

This dynamic method masks data as it is transferred from production systems to test or development systems, maintaining a synchronised masked version with production data. This is particularly useful for organisations that frequently deploy software and need to stream data to multiple testing environments.?

Deterministic data masking?

This technique consistently replaces one data value with another of the same type whenever it appears, such as replacing a real name with a fictitious one throughout a database.??

Statistical data obfuscation?

Statistical data obfuscation relies on stochastic perturbations — an algorithmic way of optimising systems with numerous unknown parameters. Differential privacy?is an example of a statistical data obfuscation system; it shares information about a dataset by describing its general patterns without relinquishing any privileged information about the individuals in the dataset — protecting privacy without impacting overall results.?

If you are interested in finding out more about data masking and how your organisation can use it to maintain compliance with data regulations, send me a message.??

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