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Data masking

 It works by replacing real data, such as personally identifiable information (PII), protected health information (PHI), or financial details, with realistic but fictitious alternatives. These masked values retain the same format, length, and structure as the original data, allowing systems and applications to function normally while minimising the risk of exposure.

By rendering the actual data inaccessible to unauthorised users, data masking ensures that sensitive information remains protected even when shared across non-secure environments like development, testing, or analytics.

Main types of data masking

There are several types of data masking, each tailored to different use cases and security needs. Let’s look at them in more detail:

Static Data Masking (SDM)

A static data masking process allows to create a masked version of production data that is stored and used offline. This permanently altered copy is ideal for development, testing, or training environments where live data isn’t required.

Dynamic Data Masking (DDM)

Dynamic data masking masks data in real time as it is accessed, without changing the underlying database. It’s typically used to restrict access based on user roles or permissions within live applications.

On-the-Fly Masking

Fly data masking applies masking rules during the real-time transfer of data between environments. This method enables secure data movement without storing unmasked data at any point.

Each type offers unique advantages and limitations. Which one to choose depends on the specific regulatory, operational, and security requirements of your organisation.

Common data masking techniques

A variety of data masking tools and techniques are available to help organisations protect sensitive information while maintaining usability and compliance.

Common techniques include:

• Substitution which replaces real values with realistic, fictitious alternatives (e.g., replacing a real name with a different, believable name).
• Shuffling which randomly rearranges data within a column to obscure individual identities while preserving overall distribution.
• Number/date variance which introduces controlled random variation to numeric or date values, keeping the data useful for analysis while hiding the original values.
• Nulling which removes or obscures all or part of a sensitive field, such as displaying only the last four digits of a credit card number.
• Encryption or tokenisation which transforms sensitive data using format-preserving encryption or tokens, allowing reversibility only with proper authorisation.

Choosing the right technique, or a combination of them, depends on the specific use case, such as whether the data will be used for analytics, testing, or operational purposes.

When selecting an optimal data masking solutions, organisations should evaluate factors like data quality, data integrity, regulatory compliance, and scalability, always taking into consideration that a well-chosen approach ensures sensitive data remains protected without compromising the functionality or performance of business processes.

Main business benefits of data masking processes

Implementing data masking delivers several strategic benefits that go beyond compliance, helping organisations manage risk, reduce costs, and drive innovation. The reasons that make data masking important are as follows:

Risk reduction

Data masking minimises the risk of data breaches and misuse by internal users, third-party vendors, or cyber attackers. By ensuring that sensitive information is never exposed in non-secure environments, it significantly lowers the chances of accidental leaks or malicious exploitation.

Cost avoidance

Avoiding data breaches means avoiding the high costs associated with regulatory fines, legal actions, and breach remediation. Data masking supports compliance with data privacy laws such as GDPR, HIPAA, and CCPA, helping organisations steer clear of costly penalties and reputational damage.

Operational utility

Masked datasets maintain their structural integrity and realism, enabling secure use in software development, testing, data analysis, and AI model training. This allows teams to innovate and optimise systems without relying on live data, accelerating development cycles while keeping security intact.

Data masking challenges businesses should plan for

While data masking offers significant benefits, it also presents a few technical and operational challenges that businesses should proactively address.

Below we look at them in more detail, checking how to overcome them.

Maintaining referential integrity across tables

When masking data across multiple related tables, it’s essential to preserve relationships – such as between customer IDs or foreign keys – to avoid breaking functionality. This can be managed by using consistent masking rules and advanced masking tools that support referential integrity across datasets.

Managing performance impacts

Data masking, particularly in dynamic or large-scale environments, can introduce latency or processing overhead. To mitigate this, organisations should optimise masking algorithms, leverage high-performance infrastructure, and schedule masking operations during low-traffic periods when possible.

Data discovery and classification

Identifying which data needs masking – especially in complex or legacy systems – can be time-consuming. Implementing automated data discovery and classification tools helps organisations accurately map sensitive data and streamline the masking process from the outset.

Handling complex, large-scale environments

In enterprises with diverse systems, cloud platforms, and databases, consistent masking across the ecosystem can be challenging. Businesses can overcome this by adopting scalable, centralised data masking solutions that integrate with multiple technologies and support both structured and unstructured data.

Best practices for implementing data masking solution

If your organisation is keen to ensure a successful data masking implementation, take into consideration these data masking best practices:

• Use a combination of masking techniques – tailor masking methods to data types and sensitivity levels by combining approaches such as substitution, shuffling, and encryption for enhanced security and usability.
• Implement role-based access control (RBAC) – enforce least privilege by restricting access to masked and unmasked data based on user roles, reducing risks from internal threats or accidental exposure.
• Audit and monitor masking activities – maintain detailed logs and conduct regular audits to track data access and detect anomalies, supporting compliance and internal security policies.
• Maintain data quality and integrity – ensure masked data remains realistic and consistent across related datasets to preserve application functionality and reporting accuracy.
• Ensure scalability and flexibility – choose solutions that scale with growing data volumes and evolving environments – such as hybrid clouds and multi-database architectures – to future-proof your masking strategy.