Why cybersecurity needs data masking

The link between data masking and cybersecurity is more relevant than ever. 

With data breaches becoming a major risk for organizations, compliance with data protection regulations becomes not only a legal obligation: it is a crucial measure to protect a business from dramatic economic losses, while also safeguarding its reputation and retaining client trust. 

In this context, having the right data masking policies in place has proven an invaluable ally, specially in securing pre-production and testing environments. Wondering why cybersecurity needs data masking and what advanced tools are available today to implement data masking?

Let’s take a look at data masking as a key move towards compliance and data protection.

What is data masking? 

Data masking is the process of transforming data in order to guarantee the confidentiality of the information it contains, while also maintaining data’s validity for software development and testing.

As a result, a new dataset emerges where sensitive information has been replaced, so that every measure has been taken to prevent unauthorized accesses. This means replacing Personally Identifiable Information (PII), Protected Health Information (PHI) and other categories of data classified as sensitive by data privacy regulations. 

While data masking is not useful for production scenarios where real data must be leveraged, it’s crucial for compliance and security in testing, software development, analytics, or ML.

The following key principles of data masking in cybersecurity explain this outstanding advantage of data masking in such contexts:

• Data masking helps protect sensitive data from unauthorized access by replacing or obscuring original values. Depending on the implementation, it can support pseudonymization—where data can be re-identified under controlled conditions—or anonymization, where re-identification is not possible. These distinctions are relevant from a legal and compliance standpoint.
• It facilitates compliance with data protection regulations such as GDPR and data privacy laws in the USA. While protecting data privacy, data masking also allows for potential auditing and compliance checks.
• Masked data remains to mirror production conditions (minus the exposure risks), so that it maintains the representativeness, coherence, and functional richness of the original data. This means masked data is useful for non-production environments.

The role of data masking in cybersecurity

It prevents data breaches and unauthorised access

By replacing real data with fictitious data, data masking means sensitive information is not compromised even if a breach occurs. A particularly important movement for contexts where, traditionally, security measures have been weaker, such as development and testing.

Additionally, data masking in cybersecurity is also effective against insider threats: scenarios where employees or contractors might potentially misuse sensitive data, accidentally or otherwise.

As such, data masking acts as an additional security layer, setting up defenses so that even if other security measures fail, sensitive data can remain protected.

Ensuring compliance with GDPR, CCPA, and other regulations

Across the globe, data privacy regulations have emerged to grant citizens further rights and protection when it comes to their personal data. The General Data Protection Regulation (GDPR) stands out as the more comprehensive data privacy regulation today, but other laws are appearing in the US and beyond, such as the California Privacy Rights Act (CPRA), with the same aim: to limit exposure of citizens’ personal data.

Among other measures, compliance with such regulations involves applying data masking for cybersecurity purposes. Failure to do so, in fact, can result in significant fines. For instance, lack of GDPR compliance can reach up to 20 million euros or 4% of the company’s total annual turnover (whatever quantity is higher).

In this context, data masking stands out as a crucial measure for compliance, as these regulations recognize it as a valid strategy for data privacy. For instance, while the GDPR doesn’t explicitly mention data masking, article 32 of this regulation mentions how organizations “shall implement appropriate technical and organisational measures to ensure a level of security appropriate to the risk, including inter alia as appropriate: the pseudonymisation and encryption of personal data”. 

The distinct advantages of data masking (compared to other privacy measures, as discussed below) make it a key strategy for GDPR compliance.

Protecting sensitive data in non-production environments

Development and non-production environments have become a key target for cyberattacks in recent times. From compromising supply chain and code libraries to code injection, threats in software development and testing are growing in quantity and complexity.

A trend that has been growing as far as 2020, when a report by Argon security detected “supply chain attacks grew by more than 300% in 2021 compared to 2020”, as well as unveiling serious neglect of cybersecurity in non-production environments.

In contrast, data masking in cybersecurity stands out as a key protection layer in this type of environments: it guarantees sensitive data isn’t employed in development environments, while still providing safe, usable alternatives for developers and testers. 

It thus stands out as a key proactive measure against accidents, human errors or intentional misuse in non-production, all while complying with data privacy regulations.

You might be interested: Why it is important to protect development environments

Different data masking techniques

Static data masking (SDM) vs. dynamic data masking (DDM)

Static and dynamic data masking offer two distinct approaches, each with their own pros and cons. Each concept’s definition provides a glimpse into their main differences:

• Static data masking involves creating a permanently masked copy of the original dataset. This new masked dataset retains the context and referential integrity of the original data, so that it can be used in non-production environments. The original data, on the contrary, is safely stored in a separate environment, ensuring sensitive data is effectively protected and cannot be easily retrieved.
• Dynamic data masking works by replacing sensitive data in transit and in real time based on defined permissions and user profiles. In practice, dynamic data masking allows users with certain user roles to decide what sensitive information must be revealed for each required purpose. Meanwhile, original data remains unchanged in the database, including all sensitive data.

Tokenization and encryption compared to data masking

Tokenization, encryption and data masking all offer valid but different methodologies for protecting sensitive data. Owing to their fundamental differences, each serves a different purpose and is used in particular contexts.

Encryption, on the one hand, is based on applying complex encryption algorithms that protect data. When comparing data masking against encryption, the following two differences emerge:

• Encrypted data can be decrypted by those who hold the encryption key, meaning the dataset is not irreversibly transformed.
• This type of data cannot be employed in testing or data analyzing activities, as it doesn’t mirror the key characteristics in the original data.

Regarding tokenization, it involves replacing sensitive data elements with tokens or random data. When comparing tokenization against data masking for cybersecurity, it’s important to understand that, while real data is safely stored, tokenization is designed to be reversed, provided an user has access to the credentials that allow him to.

As such, while tokenization and encryption both offer strong data security, data masking offers unique advantages for development and testing environments, including:

• It remains useful, as it preserves the original data’s structure and behavior.
• Is effective in protecting data even against internal threats, as it’s designed as an irreversible process.

Best practices for effective data masking

• Optimize sensitive data discovery. Any data masking process should start with a thorough data discovery process that locates PII, PHI, and other sensitive data. As seen below, current advances in AI are complementing conventional methods for making this process more comprehensive and automated. 

The discovery process should be followed by building a centralized data repository, where data from all sources and environments is classified and unified. This enhances visibility of sensitive data and thus provides the right foundations for securing it.

• Ensure the choice of the right data masking for cybersecurity. There’s a wide variety of data masking techniques available. As such, selecting the most adequate data masking technique, according to the environment and specific uses, represents a key step for effective data masking in cybersecurity.
• Test data masking. Before employing masked data, testing should confirm it is effectively protected as well as useful for its intended use.

Why businesses must prioritise data masking for cybersecurity

Growing cyber threats and data vulnerability

The leading security firm Kaspersky recently revealed a 26% surge in phishing attempts worldwide in 2024, and a 14% increase in the detection of malicious files compared to the previous year.

Just two figures in a sea of reports and news that point towards the same worrying reality: cyberattacks are becoming more frequent, sophisticated and destructive.

In this context, the question of why cybersecurity needs data masking is more evident than ever, as data masking offers one more layer of protection and is particularly useful for vulnerable environments such as non-production. 

Cost of data breaches and reputation damage

Costs related to data breaches imply such scenarios represent serious economic missteps. For instance, IBM estimates that the average cost of a data breach in 2024 escalated to USD 4.88 M, a 10% increase when compared to the previous year.

Economic costs derive from potential fines by regulatory bodies and legal fees, as well as the costs of getting businesses back to operation. On top of these expenses, the reputational costs must be accounted for: data breaches erode customers’ trust in an organization, which is seen as incapable or unwilling to protect citizens’ data.

As such, data masking in cybersecurity represents a key move towards proactive data protection, reducing breach risks while also showing commitment towards data privacy.

How data masking fits into a broader cybersecurity strategy

Data masking practices must be understood as part of the cybersecurity measures involved with protecting an organization’s information. 

At a broad level, it adds a layer of protection in environments where other security measures could be bypassed. It also protects organizations against the ‘human error’ factor that is implied in a multitude of cyberattacks.

Additionally, data masking can be crucial in supporting certain cyber security protocols. For instance, it is key for enforcing zero-trust policies, ensuring sensitive data can’t be easily accessed unless authorized.

The future of data masking in cybersecurity: emerging trends and innovations

Data masking in cybersecurity is evolving to help organizations navigate increasingly complex and risky digital environments. In this regard, cloud-native solutions for data masking are emerging, and so is advanced dynamic data masking as a solution for certain scenarios where context-specific data is required.

This trend coexists with the rise of comprehensive compliance tools, where data masking coexists with other measures such as tokenization, encryption or pseudonymization. This allows for designing dynamic strategies that are based on context and data sensitivity.

The need for faster, more secure software delivery and analytics is also guiding important developments in masking tools that integrate with ease with the broader digital ecosystem.

Finally, a look at ‘Market guide for data masking and synthetic data’, reported by Gartner, reveals advancements in AI and automation are bringing forward a new era for data masking in cybersecurity. More specifically: 

• AI-driven data masking is expected to enhance security. AI is being leveraged to discover sensitive data precisely, classify it and apply an adequate masking technique. A key movement to avoid human errors and ensure compliance in increasingly complex data environments.
• The role of automation for agile and increased data protection. Automation in discovery and masking processes allows for significant reductions in test data preparation time, thus accelerating development times and enhancing security.

In this context, icaria TDM emerges as a key ally for data privacy in non-productive environments. The tool allows organizations to apply data masking for cybersecurity, but its functionalities extend beyond traditional data masking or pseudonymization functions.

Building from the TDM methodology, the platform ensures data integrity and confidentiality in non-production environments, offering compliance, automation, integration with other tools and advanced sensitive data mapping.

Boasting an extensive catalog of masking and pseudonymization algorithms, icaria TDM allows organizations to leverage test data according to their needs while ensuring compliance and data privacy.

Want to learn more about data masking for cybersecurity and how to implement it in your organization? 

At icaria Technology, we can help you. Discover more about us and get in touch with our team!