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With virtually everything gone digital, data is the driving force behind today’s businesses and the global economy. The exponential growth of data generation and storage has put protecting private and sensitive information at the forefront.
According to an IBM report, the global average cost of a data breach reached a staggering $4.45 million in 2023. In the United States, that number is more than double, with data breaches costing businesses $9.48 million on average. These stark figures highlight the urgent need for effective data security and protectionary measures.
Enter data masking—a powerful technique that’s rapidly gaining traction in the cybersecurity landscape. As organisations grapple with elaborate cyber-attacks and stringent data protection regulations like GDPR and CCPA, data masking offers a proven strategy to help safeguard sensitive information while maintaining its utility for business.
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What Is Data Masking?
Data masking, also known as data obfuscation or data anonymisation, is a cybersecurity technique that generates a fundamentally similar but artificial form of an organisation’s data. The idea is to replace sensitive information with realistic but unrecognisable data, thereby protecting it from data theft while preserving the original dataset’s integrity.
The goal of data masking is to safeguard confidential information like personally identifiable information (PII), financial records, or proprietary business data. By obscuring or altering particularly sensitive elements, data masking enables organisations to utilise masked data for non-production purposes, such as analytics reporting, software testing, or third-party sharing, without revealing actual sensitive information.
Data masking has become an increasingly useful and relied-upon cybersecurity measure. In addition to mitigating the risk of data breaches, it helps organisations comply with data protection regulations and maintain operative data privacy. Data masking enables organisations to employ their data assets for operational purposes while preventing the exposure of sensitive information, striking a balance between data utility and security.
How Data Masking Works
Data masking operates on a straightforward principle: replace private and sensitive data with inauthentic yet usable data while maintaining its original structure and format. This process ensures masked data remains functional yet is worthless to potential attackers or unauthorised users. Data masking’s workflow typically involves the following steps:
- Identification: The first step is to identify and classify sensitive data that demands protection within the dataset.
- Define masking rules: Based on the identified data, define specific masking rules instructing how to transform each data type.
- Transformation: The original data is then transformed according to the defined rules, which may involve techniques like encryption, substitution, or shuffling.
- Consistency maintenance: To preserve referential integrity, the masking process ensures that the same piece of sensitive data is consistently masked across all instances in the dataset.
- Validation: The masked data is validated to ensure it meets the defined rules and maintains the required format and structure.
Data masking ultimately creates a protected version of the original information that can be safely used in non-production environments while minimising the risk of accidental exposure or theft.
Types of Data Masking
Data masking can be categorised into several types based on the specific use cases and requirements. Here are the primary types:
1. Static Data Masking (SDM)
Static data masking involves manufacturing a masked copy of the original dataset, which can then be used for development, testing, and other non-production environments. The original data remains unaltered, while the masked version protects its privacy and security. SDM is useful for cases where data is shared with third parties or used in environments with less stringent security controls.
2. Dynamic Data Masking (DDM)
On the other hand, dynamic data masking masks data in real-time as users access it. Unlike static data masking, DDM does not create a separate masked copy of the dataset. Instead, this type deploys masking rules instantly, ensuring sensitive information is obscured based on the user’s role and access permissions. This type is ideal for production environments where data needs to be protected dynamically without altering the underlying database.
3. On-the-Fly Data Masking
This form of data masking is commonly applied during data transfer processes, such as data migration or replication. As data moves from one environment to another, it is masked in transit, ensuring that sensitive information is protected throughout the transfer process. On-the-fly data masking is useful for cloud migrations or data integration projects where data security is critical.
4. Deterministic Data Masking
With deterministic data masking, the same input value always results in the same masked output value. This consistency is crucial for maintaining referential integrity across different datasets or database tables. For example, if an individual’s name is masked in one table, it will appear masked in the same way in all related tables, preserving relationships and data integrity.
5. Non-Deterministic Data Masking
In contrast, non-deterministic data masking introduces an added degree of randomness into the masking process. The same input value may result in different masked output values each time it is processed. This type is ideal in cases where absolute data consistency is not required, but a stronger level of data anonymisation is needed.
Data Masking Techniques
Various data masking techniques can obscure sensitive information while maintaining its operational utility. Each technique offers specific use cases and advantages.
- Substitution: This technique involves replacing sensitive data with realistic but unrecognisable data. For example, the names of customers might be replaced with fictitious names from a predefined list.
- Shuffling: Data values are randomly rearranged within a column. This technique maintains the information’s values but alters their positions, effectively masking the original data.
- Number and date variance: This technique involves adding or subtracting a random variance to numeric or date values. For example, each digit in a Social Security number might be shifted by a random number within a specified range.
- Encryption: This common cybersecurity technique transforms sensitive data into an unreadable format using cryptographic algorithms. Only with the correct decryption key can authorised users revert data to its authentic form.
- Masking out: Masking out involves hiding sensitive data with a constant character, such as an asterisk or a series of X’s. For instance, a credit card number might be masked out as “*** **** **** 1234”.
- Nulling out: Nulling out replaces data with null values. This technique, while simple, ensures no sensitive information is exposed. However, it’s not always practical in scenarios where the presence of data is needed for operational purposes.
- Redaction: This technique involves removing sensitive data entirely or replacing it with a placeholder. For example, an email address might be redacted to “user@domain.com”.
- Tokenisation: Data can be replaced with unique tokens with no exploitable value. The original data is stored securely in a separate location, and the tokens are used in its place.
- Blurring: This data masking method adds a controlled level of inaccuracy to sensitive information, making it less precise but still useful. For example, an exact birthdate might be blurred to only show the year, helping protect privacy while retaining the utility of the data.
- Pseudonymisation: Here, identifiable information is replaced with pseudonyms or aliases. Pseudonymised data can be re-identified if necessary, using additional information stored separately.
Choosing the most appropriate data masking technique depends on the specific requirements and constraints of the use case, ensuring that data security is balanced with operational needs.
Benefits of Data Masking
As a fundamental form of data protection and security, data masking offers many benefits to organisations while providing peace of mind to customers and end users.
- Enhanced security: Data masking dramatically reduces the risk of data breaches by ensuring that sensitive information is concealed and unrecognisable, making it useless to cyber-attackers.
- Regulatory compliance: By anonymising personal data, data masking helps organisations comply with data regulations like GDPR and CCPA, minimising the risk of legal penalties.
- Data privacy: Sensitive information, including PII and private financial data, is kept confidential, establishing trust among customers and stakeholders.
- Cost-effectiveness: Data masking is often a more economical solution than encryption. It lowers potential costs associated with data breaches, including legal fees and reputational damage.
- Safe data sharing: Organisations can share data with third parties without the risk of exposing sensitive information, enabling safe collaboration while maintaining data security.
- Improved testing and development: Developers can operate using realistic datasets without risking exposure to sensitive data.
- Maintaining data utility: Masked data retains its original structure, which enables organisations to continue utilising information without compromising security.
- Reduced insider threats: By limiting access to sensitive information and concealing its actual contents, data masking drastically reduces the risk of misuse, data leaks, or unauthorised access by internal users.
- Flexibility and scalability: Data masking techniques can be leveraged in various ways to accommodate specific organisational needs and can be scaled to handle growing datasets and evolving requirements.
- Enhanced data governance: Creating data masking policies supports robust data governance practices, ensuring consistent and reliable protection of sensitive information across the entire organisation.
While data masking has many benefits, organisations are often met with certain challenges to overcome.
Challenges of Data Masking
As mentioned previously, data masking provides significant benefits but also poses several challenges that organisations must navigate.
Maintaining Data Integrity and Referential Consistency
Preserving the integrity and relationships between data elements is crucial. Organisations must ensure that masked data maintains primary and foreign key relationships and remains consistent across all instances. Failure to do so can lead to unusable datasets for testing or analytics.
Performance and Scalability
Data masking can be a resource-intensive process, particularly with large datasets. Organisations may experience performance degradation during the masking process and face difficulties when scaling solutions to handle increasing data volumes, potentially delaying development cycles.
Identifying and Classifying Sensitive Data
Accurately identifying which data needs masking is complex. Organisations often struggle to analyse vast datasets, especially unstructured data and must stay updated with evolving privacy regulations to ensure proper classification and compliance.
Balancing Security with Data Utility
A common challenge is to establish an optimal balance between protecting sensitive data and maintaining its usefulness. Organisations must ensure that masked data retains its statistical properties while being sufficiently protected against potential reconstruction attacks.
Data Masking Best Practices
Effective data masking demands follow several best practices to ensure optimal security, compliance, and utility. Some of the most fundamental best practices include:
- Proper identification and classification of sensitive data: Before data can be properly masked, it’s critical to identify and classify sensitive information across all storage and processing environments. This involves determining where sensitive data is stored, how it can be used, and who can access it.
- Choose the appropriate masking techniques: A critical best practice of data masking is choosing a technique based on the type of data and its usage context. Data masking techniques such as substitution, shuffling, and encryption should be selected to balance data security with utility.
- Maintain referential integrity: Preserving the referential integrity between data elements is critical for maintaining the functionality of applications and analytics that rely on these relationships. Techniques like deterministic masking can help maintain consistency across datasets, preventing data corruption.
- Secure access to masked data: Access to masked data should be controlled and monitored to prevent unauthorised access. Implement role-based access controls (RBAC) to ensure that only designated personnel can view masked data and use secure masking algorithms to prevent reverse engineering or unauthorised decryption.
- Test and validate masking techniques: Before deploying data masking techniques in production, extensively test and validate them to ensure they provide the necessary level of security and functionality. Regularly assess the effectiveness of masking methods and adjust them as needed to address any vulnerabilities or compliance gaps.
- Ensure scalability and repeatability: Data masking solutions should be scalable to handle increasing data volumes and changing business demands. Repeatable masking processes ensure that new data can be consistently masked without extensive reconfiguration.
By following these best practices, organisations can leverage effective data masking strategies that check all the necessary boxes for security, utility, compliance, and scalability.
How Proofpoint Can Help
Proofpoint offers comprehensive cybersecurity solutions designed to address the complex challenges of data protection and privacy. The company’s Information & Data Protection Management solution takes a people-centric approach to data security, focusing on the human element—often the weakest link in data protection strategies. By leveraging advanced AI and machine learning technologies, Proofpoint’s solutions can identify sensitive data across various communication channels, including email, cloud applications, and endpoints, ensuring that organisations’ critical information remains secure.
Proofpoint’s Data Loss Protection and Prevention capabilities go beyond traditional methods by incorporating behavioural analytics to detect and prevent both accidental and malicious data exfiltration attempts. These solutions enable organisations to implement privacy by design principles, ensuring compliance with regulations like GDPR and CCPA while maintaining data utility.
With Proofpoint, organisations can confidently mask sensitive data, enforce encryption policies, and gain visibility into user behaviour patterns that may pose risks to data security. Partnering with Proofpoint provides a holistic approach to information protection that adapts to the evolving threat landscape and regulatory environment. For more information, contact Proofpoint.