Data Masking: What It Is and 8 Ways To Implement It
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TL;DR

This article details the importance of data masking and evaluates some strategies organisations can leverage to implement the practice, including the most helpful tools. It then covers the most prevalent challenges companies must overcome as they execute data masking and gives some helpful solutions. 

Introduction 

Credit card numbers, personal health information, intellectual property — companies possess vast amounts of sensitive data that must be protected from threat actors. Firewalls and password settings may suffice for some data, but you must safeguard other information so that even if malicious actors access it, the objects it represents are still secure. Data masking helps you do just that. 

Data masking, or obfuscation, creates a fake yet realistic version of your data. It does this through substituting, encrypting, mapping, or redacting specific values while possibly swapping them with false ones. The aim is to maintain your data integrity so that it's still useful for your analysis while rendering it useless to outsiders. 

In this article, we'll answer "What is data masking?" and dive deeper into its application. We'll explore the data masking process by looking at the different data types, how best to mask them and the tools needed to pull it off. We'll also examine common challenges data masking presents and their solutions and discuss some important regulations that apply to the process. Then we'll show you what the future looks like for data masking and how it can give your business a competitive edge. 

Key Takeaways 

  1. The data masking process alters real data by replacing it with fictitious but realistic substitute data. 
  2. Masking data helps companies secure sensitive information, enhance user privacy and better adhere to regulatory requirements. 
  3. The most common types of data masking are static data masking, dynamic data masking, on-the-fly data masking and deterministic data masking. 
  4. The main data masking techniques include anonymisation, substitution, encryption, redaction, shuffling, averaging and date switching — all of which are a form of pseudonymisation. 
  5. Some key challenges associated with effective data masking implementation are preserving data integrity for analysis, maintaining adequate security and balancing the need for security with accessibility. 
  6. To overcome these challenges, organisations should establish the scope of their data masking project, preserve their data's referential integrity and secure their data masking algorithms to prevent reverse engineering. 
  7. The two regulations that govern data masking practices the most are the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Others such as NIST 800-53, ISO:27001, PCI DSS and HIPAA also exist.
  8. The future of data masking technology lies in leveraging AI capabilities for better automation and the coupling of data masking with other Privacy Enhancing Technologies (PET).

Introduction to Data Masking 

Organisations often possess data they must use for analytics purposes, but that comes with the risk of being viewed by the wrong eyes. Improper disclosure could result in the loss of valuable intellectual property, costly compliance violations and loss of consumer trust. 

One way to avoid the risks of improper disclosure is to create a false dataset that resembles the original one. That way, it will still bear the same utility for analysis but will be useless to unauthorised viewers. This process is known as data masking and it most often entails substituting the initial symbols with alternative ones, hiding the original data. 

Understanding What Data Requires Masking

Different data types have different sensitivity levels, so not all data needs to be masked. Some common types of data that organisations often mask are: 

  • Personally identifiable information (PII): Names, Social Security numbers, addresses
  • Financial information: Account numbers, credit card numbers, bank statements
  • Personal health information (PHI): Diagnoses, treatment outcomes, insurance information 
  • Internal data: Vendor records, third-party suppliers
  • Confidential/Sensitive data: Intellectual property, trade secrets, legal agreements  

While many lend themselves well to masking, certain data types might be better suited for some masking methods than others. For example, you may be able to mask names and Social Security numbers best by swapping out certain numbers or letters with alternative characters. You should redact other data types such as confidential contract agreements entirely. 

The 8 Data Masking Techniques

Because the many kinds of data types are better masked using some methods than others, it's important to be aware of the different data masking techniques that are out there. The most common types of data masking are:

  • Data Pseudonymisation: Replacing private identifiers with fake identifiers or pseudonyms to maintain data privacy while retaining data utility
  • Data Anonymisation: Completely stripping data of identifying information, making re-identification impossible
  • Lookup Substitution: Using a lookup table to replace sensitive data with non-sensitive equivalents
  • Encryption: Encoding data to obscure its original content, requiring a decryption key to revert to its original form
  • Redaction: Blacking out or removing text and data to prevent disclosure of sensitive information
  • Averaging: Replacing data with its average value in a set, used for numerical data to maintain privacy while preserving statistical significance
  • Shuffling: Randomising the order of data in a dataset to sever the link between data elements and their owners
  • Date Switching: Altering date values within a dataset to protect sensitive temporal information while maintaining the sequence and interval integrity

Note that data pseudonymisation is a term created by the General Data Protection Regulation (GDPR) to denote replacing private identifiers with false identifiers to maintain data privacy, so nearly all other data masking techniques are a form of pseudonymisation. 

Implementing Data Masking 

Once you've chosen which data masking techniques you'll use, you can decide on a schema for implementation and best practices for launching your system. 

Data Masking Methods

The first step is to select a method for masking your data. The four data masking schemas are:

  • Deterministic data masking: With this method, you assign the same value to every data item possessing a given value. For example, every account with the last name "Smith" would be changed to "Brown" to protect user confidentiality. 
  • Static data masking (SDM): Rather than apply the same value to each item, SDM applies a predetermined set of criteria to your data while it remains in its initial repository. 
  • Dynamic data masking (DDM): While SDM focuses on masking data in its initial data location and transfers it afterwards as needed, DDM applies your masking algorithms to your data as it's streamed in real-time.
  • On-the-fly data masking: Similar to DDM, on-the-fly data masking obfuscates information as it's transferred from one location to another. The difference is that on-the-fly masking is applied to data in transit between two storage systems, while DDM is streamed without a final repository. 

Another key data masking method is statistical obfuscation. By applying given functions or perturbation methods to the elements within a dataset, you can map your data from one form to another. However, as with other data masking techniques, it's essential to keep your obfuscation algorithm secure — one of the main challenges to data masking.

Best Practices

After selecting a data masking method, organisations should implement data masking best practices to ensure their system works as planned. Some best practices include:

  • Determining the project scope: Decide what data should be masked, who should have access to the original version and what algorithms should be used to mask it. 
  • Using the same algorithms: Ensure consistency across all departments by using the same algorithms for each data type. This is known as referential integrity. 
  • Securing your algorithms: Protect your data from reverse engineering if it falls into the hands of threat actors.

After selecting the proper method and implementing best practices into your workflows, shape your data masking strategies around the standards, frameworks and requirements that apply to your industry. Then, you can choose a tool that will help you mask your data most effectively.

Regulatory Compliance 

One of the biggest benefits of data masking is that it helps companies improve their compliance. By altering fields that would reveal a customer's identity, data masking enhances consumer privacy which many regulations are in place to protect. That allows companies to avoid costly compliance violations, improving profitability. 

For example, the General Data Protection Regulation (GDPR) requires that companies pseudonymise enough of their customers' data that their clients' identities would be impossible to decipher if their data were breached. The California Consumer Privacy Act (CCPA) also stipulates that customers have the "right to be forgotten", which can be achieved through data nulling once their information has been used as needed. 

The Top 5 Tools and Technologies for Data Masking

Given the massive amounts they generate, manually altering a company's data isn't feasible. That means data masking technologies are a must. Many software reviewers have given input on the best tools that can help organisations mask their data. However, the exact tool you use will depend on your own organisational needs. 

This list isn't a formal ranking, but here's a brief overview of some of the top data masking tools and technologies:

  • Informatica Cloud Data Masking: Good for cloud-based applications
  • Microsoft Azure SQL Server: Good for SQL-based environments
  • Baffle: Good for encryption-based masking
  • Mage: Good for the financial and healthcare industry
  • Delphix: Good for masking in non-production environments

As you decide which tool is right for your company, consider both your application and your existing tech stack. A no-code platform may be more user-friendly and could facilitate your data masking efforts, but more advanced solutions such as masking tools for big data may require some code.

Challenges and Solutions in Data Masking 

Even if you implement best practices and use the best tools available, you'll likely encounter some challenges to effective data masking. These are the main data masking hurdles companies must clear and the solutions that can help. 

Attribute preservation: Your masked data must tell the same story as the initial data to be of any analytical use. Altering certain data fields may make it difficult to discern the meaning of your original data, making data-driven decision-making more difficult. some text

Solution: Maintain your data's integrity by only making alterations that can be mapped to the original state using the proper algorithm, or by making adjustments that don't skew your data's attributes.

Semantic meaning: Masked data should adhere to an organisation's business rules and formatting regulations. One example is an identification number using the required number of alphanumeric characters. Otherwise, a compliance violation may occur.

Solution: Only adjust data according to predefined standards and regulations, so it remains consistent with its initial format.

Integration with current workflows: Some data masking technologies may not be compatible with your current environment and may come with a steep learning curve for your employees.

Solution: Use the most intuitive data masking tools possible and provide ample training to your employees, prioritising compatibility.

Data masking is a relatively simple process, but the challenges arise when your dataset loses its formatting consistency. Make sure its attributes and semantic arrangement map over correctly. Then the only remaining challenge is finding a tool that plays well with the rest of your stack.

The Future of Data Masking 

Data masking technologies continue to evolve, especially as AI/ML algorithms are refined. Data masking tools with AI functionalities can detect sensitive information that must be masked while automation features minimise human intervention. The result is fewer errors, faster data management processes, greater security and a data team that has time for more value-added tasks.

Another feature component of data masking is privacy-enhancing technologies (PET). PETs function as software functionalities that drive business value while augmenting data privacy. You can use them with data masking tools to further strengthen your cybersecurity posture. Examples include multi-party computation and oblivious proxies, which divide your data into smaller increments and distribute it across multiple servers. PETs and data masking technologies can render your data assets virtually useless to threat actors.

Conclusion 

As the threat landscape grows increasingly complex, the risk of a data breach grows too. Some business processes can carry on even if data is compromised, but other data is more mission-critical. You should always obfuscate data to protect your key operations and customers.

Data masking achieves this heightened security and alongside PETs, forms a critical piece of a business' broader data security puzzle. Despite some challenges, data masking is a simple yet effective way to mitigate the risk of a breach. It can even create additional revenue streams, as you can leverage your enhanced data privacy to monetise your newly-masked datasets without the risk of reverse engineering. 

Stronger security, improved privacy, better compliance — data masking can do all this and more for your organisation. Stay current on data masking and other security solutions with Zendata.

FAQ

1. How Do Advanced Data Masking Techniques Affect Database Performance, Especially in Large-Scale Environments?

Advanced data masking, particularly dynamic and on-the-fly techniques, can impact database performance due to the extra processing required to mask data in real-time. In large-scale environments, this impact can be mitigated by optimising masking algorithms for efficiency and selectively applying dynamic masking to only the most sensitive data, ensuring a balance between data security and system performance.

2. In the Context of Data Breach Incidents, How Effective Is Data Masking in Mitigating Potential Damages?

Data masking is crucial in reducing the potential damage from data breaches by ensuring that exposed data is either anonymised or pseudonymised, making it less useful to attackers. However, its effectiveness is contingent upon the implementation quality and the combination of masking techniques, such as encryption and substitution for sensitive data and PII.

3. How Can Organisations Leverage Pseudonymisation and Anonymisation in Light of Cookie Deprecation and the Rise of First-Party Data?

With the shift towards first-party data due to cookie deprecation, organisations can use pseudonymisation and anonymisation to comply with privacy regulations like GDPR while still gaining valuable insights from their data. By applying these techniques, companies can protect user privacy by masking identifiers, ensuring that data remains useful for analysis without compromising individual privacy.

4. What Role Does Encryption Play in Enhancing the Security of Masked Data During Transit Between Different Storage Systems?

Encryption is critical in securing masked data during transit by providing an additional layer of security that complements data masking. When data is transferred between storage systems, encryption ensures that even if data interception occurs, the masked (and thus anonymised or pseudonymised) data remains protected against unauthorised access, bolstering overall data security.

5. How Do Recent Advances in Privacy-Enhancing Technologies (PETs) Complement Traditional Data Masking Techniques?

Recent advances in PETs, such as secure multi-party computation and differential privacy, offer new ways to protect data privacy and complement traditional data masking techniques like anonymisation and pseudonymisation. By integrating PETs with data masking, organisations can enhance their ability to secure sensitive information and PII against unauthorised access while still enabling data to be useful for analysis and decision-making.

 

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Data Masking: What It Is and 8 Ways To Implement It

March 27, 2024

TL;DR

This article details the importance of data masking and evaluates some strategies organisations can leverage to implement the practice, including the most helpful tools. It then covers the most prevalent challenges companies must overcome as they execute data masking and gives some helpful solutions. 

Introduction 

Credit card numbers, personal health information, intellectual property — companies possess vast amounts of sensitive data that must be protected from threat actors. Firewalls and password settings may suffice for some data, but you must safeguard other information so that even if malicious actors access it, the objects it represents are still secure. Data masking helps you do just that. 

Data masking, or obfuscation, creates a fake yet realistic version of your data. It does this through substituting, encrypting, mapping, or redacting specific values while possibly swapping them with false ones. The aim is to maintain your data integrity so that it's still useful for your analysis while rendering it useless to outsiders. 

In this article, we'll answer "What is data masking?" and dive deeper into its application. We'll explore the data masking process by looking at the different data types, how best to mask them and the tools needed to pull it off. We'll also examine common challenges data masking presents and their solutions and discuss some important regulations that apply to the process. Then we'll show you what the future looks like for data masking and how it can give your business a competitive edge. 

Key Takeaways 

  1. The data masking process alters real data by replacing it with fictitious but realistic substitute data. 
  2. Masking data helps companies secure sensitive information, enhance user privacy and better adhere to regulatory requirements. 
  3. The most common types of data masking are static data masking, dynamic data masking, on-the-fly data masking and deterministic data masking. 
  4. The main data masking techniques include anonymisation, substitution, encryption, redaction, shuffling, averaging and date switching — all of which are a form of pseudonymisation. 
  5. Some key challenges associated with effective data masking implementation are preserving data integrity for analysis, maintaining adequate security and balancing the need for security with accessibility. 
  6. To overcome these challenges, organisations should establish the scope of their data masking project, preserve their data's referential integrity and secure their data masking algorithms to prevent reverse engineering. 
  7. The two regulations that govern data masking practices the most are the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Others such as NIST 800-53, ISO:27001, PCI DSS and HIPAA also exist.
  8. The future of data masking technology lies in leveraging AI capabilities for better automation and the coupling of data masking with other Privacy Enhancing Technologies (PET).

Introduction to Data Masking 

Organisations often possess data they must use for analytics purposes, but that comes with the risk of being viewed by the wrong eyes. Improper disclosure could result in the loss of valuable intellectual property, costly compliance violations and loss of consumer trust. 

One way to avoid the risks of improper disclosure is to create a false dataset that resembles the original one. That way, it will still bear the same utility for analysis but will be useless to unauthorised viewers. This process is known as data masking and it most often entails substituting the initial symbols with alternative ones, hiding the original data. 

Understanding What Data Requires Masking

Different data types have different sensitivity levels, so not all data needs to be masked. Some common types of data that organisations often mask are: 

  • Personally identifiable information (PII): Names, Social Security numbers, addresses
  • Financial information: Account numbers, credit card numbers, bank statements
  • Personal health information (PHI): Diagnoses, treatment outcomes, insurance information 
  • Internal data: Vendor records, third-party suppliers
  • Confidential/Sensitive data: Intellectual property, trade secrets, legal agreements  

While many lend themselves well to masking, certain data types might be better suited for some masking methods than others. For example, you may be able to mask names and Social Security numbers best by swapping out certain numbers or letters with alternative characters. You should redact other data types such as confidential contract agreements entirely. 

The 8 Data Masking Techniques

Because the many kinds of data types are better masked using some methods than others, it's important to be aware of the different data masking techniques that are out there. The most common types of data masking are:

  • Data Pseudonymisation: Replacing private identifiers with fake identifiers or pseudonyms to maintain data privacy while retaining data utility
  • Data Anonymisation: Completely stripping data of identifying information, making re-identification impossible
  • Lookup Substitution: Using a lookup table to replace sensitive data with non-sensitive equivalents
  • Encryption: Encoding data to obscure its original content, requiring a decryption key to revert to its original form
  • Redaction: Blacking out or removing text and data to prevent disclosure of sensitive information
  • Averaging: Replacing data with its average value in a set, used for numerical data to maintain privacy while preserving statistical significance
  • Shuffling: Randomising the order of data in a dataset to sever the link between data elements and their owners
  • Date Switching: Altering date values within a dataset to protect sensitive temporal information while maintaining the sequence and interval integrity

Note that data pseudonymisation is a term created by the General Data Protection Regulation (GDPR) to denote replacing private identifiers with false identifiers to maintain data privacy, so nearly all other data masking techniques are a form of pseudonymisation. 

Implementing Data Masking 

Once you've chosen which data masking techniques you'll use, you can decide on a schema for implementation and best practices for launching your system. 

Data Masking Methods

The first step is to select a method for masking your data. The four data masking schemas are:

  • Deterministic data masking: With this method, you assign the same value to every data item possessing a given value. For example, every account with the last name "Smith" would be changed to "Brown" to protect user confidentiality. 
  • Static data masking (SDM): Rather than apply the same value to each item, SDM applies a predetermined set of criteria to your data while it remains in its initial repository. 
  • Dynamic data masking (DDM): While SDM focuses on masking data in its initial data location and transfers it afterwards as needed, DDM applies your masking algorithms to your data as it's streamed in real-time.
  • On-the-fly data masking: Similar to DDM, on-the-fly data masking obfuscates information as it's transferred from one location to another. The difference is that on-the-fly masking is applied to data in transit between two storage systems, while DDM is streamed without a final repository. 

Another key data masking method is statistical obfuscation. By applying given functions or perturbation methods to the elements within a dataset, you can map your data from one form to another. However, as with other data masking techniques, it's essential to keep your obfuscation algorithm secure — one of the main challenges to data masking.

Best Practices

After selecting a data masking method, organisations should implement data masking best practices to ensure their system works as planned. Some best practices include:

  • Determining the project scope: Decide what data should be masked, who should have access to the original version and what algorithms should be used to mask it. 
  • Using the same algorithms: Ensure consistency across all departments by using the same algorithms for each data type. This is known as referential integrity. 
  • Securing your algorithms: Protect your data from reverse engineering if it falls into the hands of threat actors.

After selecting the proper method and implementing best practices into your workflows, shape your data masking strategies around the standards, frameworks and requirements that apply to your industry. Then, you can choose a tool that will help you mask your data most effectively.

Regulatory Compliance 

One of the biggest benefits of data masking is that it helps companies improve their compliance. By altering fields that would reveal a customer's identity, data masking enhances consumer privacy which many regulations are in place to protect. That allows companies to avoid costly compliance violations, improving profitability. 

For example, the General Data Protection Regulation (GDPR) requires that companies pseudonymise enough of their customers' data that their clients' identities would be impossible to decipher if their data were breached. The California Consumer Privacy Act (CCPA) also stipulates that customers have the "right to be forgotten", which can be achieved through data nulling once their information has been used as needed. 

The Top 5 Tools and Technologies for Data Masking

Given the massive amounts they generate, manually altering a company's data isn't feasible. That means data masking technologies are a must. Many software reviewers have given input on the best tools that can help organisations mask their data. However, the exact tool you use will depend on your own organisational needs. 

This list isn't a formal ranking, but here's a brief overview of some of the top data masking tools and technologies:

  • Informatica Cloud Data Masking: Good for cloud-based applications
  • Microsoft Azure SQL Server: Good for SQL-based environments
  • Baffle: Good for encryption-based masking
  • Mage: Good for the financial and healthcare industry
  • Delphix: Good for masking in non-production environments

As you decide which tool is right for your company, consider both your application and your existing tech stack. A no-code platform may be more user-friendly and could facilitate your data masking efforts, but more advanced solutions such as masking tools for big data may require some code.

Challenges and Solutions in Data Masking 

Even if you implement best practices and use the best tools available, you'll likely encounter some challenges to effective data masking. These are the main data masking hurdles companies must clear and the solutions that can help. 

Attribute preservation: Your masked data must tell the same story as the initial data to be of any analytical use. Altering certain data fields may make it difficult to discern the meaning of your original data, making data-driven decision-making more difficult. some text

Solution: Maintain your data's integrity by only making alterations that can be mapped to the original state using the proper algorithm, or by making adjustments that don't skew your data's attributes.

Semantic meaning: Masked data should adhere to an organisation's business rules and formatting regulations. One example is an identification number using the required number of alphanumeric characters. Otherwise, a compliance violation may occur.

Solution: Only adjust data according to predefined standards and regulations, so it remains consistent with its initial format.

Integration with current workflows: Some data masking technologies may not be compatible with your current environment and may come with a steep learning curve for your employees.

Solution: Use the most intuitive data masking tools possible and provide ample training to your employees, prioritising compatibility.

Data masking is a relatively simple process, but the challenges arise when your dataset loses its formatting consistency. Make sure its attributes and semantic arrangement map over correctly. Then the only remaining challenge is finding a tool that plays well with the rest of your stack.

The Future of Data Masking 

Data masking technologies continue to evolve, especially as AI/ML algorithms are refined. Data masking tools with AI functionalities can detect sensitive information that must be masked while automation features minimise human intervention. The result is fewer errors, faster data management processes, greater security and a data team that has time for more value-added tasks.

Another feature component of data masking is privacy-enhancing technologies (PET). PETs function as software functionalities that drive business value while augmenting data privacy. You can use them with data masking tools to further strengthen your cybersecurity posture. Examples include multi-party computation and oblivious proxies, which divide your data into smaller increments and distribute it across multiple servers. PETs and data masking technologies can render your data assets virtually useless to threat actors.

Conclusion 

As the threat landscape grows increasingly complex, the risk of a data breach grows too. Some business processes can carry on even if data is compromised, but other data is more mission-critical. You should always obfuscate data to protect your key operations and customers.

Data masking achieves this heightened security and alongside PETs, forms a critical piece of a business' broader data security puzzle. Despite some challenges, data masking is a simple yet effective way to mitigate the risk of a breach. It can even create additional revenue streams, as you can leverage your enhanced data privacy to monetise your newly-masked datasets without the risk of reverse engineering. 

Stronger security, improved privacy, better compliance — data masking can do all this and more for your organisation. Stay current on data masking and other security solutions with Zendata.

FAQ

1. How Do Advanced Data Masking Techniques Affect Database Performance, Especially in Large-Scale Environments?

Advanced data masking, particularly dynamic and on-the-fly techniques, can impact database performance due to the extra processing required to mask data in real-time. In large-scale environments, this impact can be mitigated by optimising masking algorithms for efficiency and selectively applying dynamic masking to only the most sensitive data, ensuring a balance between data security and system performance.

2. In the Context of Data Breach Incidents, How Effective Is Data Masking in Mitigating Potential Damages?

Data masking is crucial in reducing the potential damage from data breaches by ensuring that exposed data is either anonymised or pseudonymised, making it less useful to attackers. However, its effectiveness is contingent upon the implementation quality and the combination of masking techniques, such as encryption and substitution for sensitive data and PII.

3. How Can Organisations Leverage Pseudonymisation and Anonymisation in Light of Cookie Deprecation and the Rise of First-Party Data?

With the shift towards first-party data due to cookie deprecation, organisations can use pseudonymisation and anonymisation to comply with privacy regulations like GDPR while still gaining valuable insights from their data. By applying these techniques, companies can protect user privacy by masking identifiers, ensuring that data remains useful for analysis without compromising individual privacy.

4. What Role Does Encryption Play in Enhancing the Security of Masked Data During Transit Between Different Storage Systems?

Encryption is critical in securing masked data during transit by providing an additional layer of security that complements data masking. When data is transferred between storage systems, encryption ensures that even if data interception occurs, the masked (and thus anonymised or pseudonymised) data remains protected against unauthorised access, bolstering overall data security.

5. How Do Recent Advances in Privacy-Enhancing Technologies (PETs) Complement Traditional Data Masking Techniques?

Recent advances in PETs, such as secure multi-party computation and differential privacy, offer new ways to protect data privacy and complement traditional data masking techniques like anonymisation and pseudonymisation. By integrating PETs with data masking, organisations can enhance their ability to secure sensitive information and PII against unauthorised access while still enabling data to be useful for analysis and decision-making.