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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.
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.
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.
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:
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.
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:
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.
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.
The first step is to select a method for masking your data. The four data masking schemas are:
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.
After selecting a data masking method, organisations should implement data masking best practices to ensure their system works as planned. Some best practices include:
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.
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.
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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:
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.
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:
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.
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.
The first step is to select a method for masking your data. The four data masking schemas are:
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.
After selecting a data masking method, organisations should implement data masking best practices to ensure their system works as planned. Some best practices include:
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.
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.
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
