Key Points:

• Data masking involves replacing sensitive data with scrambled or fake data for security. It protects sensitive information at rest or in transit and aids data sharing for testing or training.
• It ensures compliance with regulations like GDPR and HIPAA, reduces data loss risks, enhances customer trust, and maintains business functionality.
• Data masking techniques include substitution, shuffling, encryption, and tokenisation.
• Metomic's data security software uses data masking to ensure that the data we hold for our customers is protected, and secure

What is data masking?

Data masking is a cybersecurity technique that protects sensitive information by replacing it with fake or randomised data. This safeguards sensitive data both at rest (stored) and in transit (being transmitted). By obscuring sensitive data, organisations can reduce the risk of data breaches, identity theft, and regulatory penalties.

Key Benefits of Data Masking:

• Enhanced Security: Protects sensitive data from unauthorized access and cyberattacks.
• Compliance Adherence: Helps organisations comply with data privacy regulations like GDPR and HIPAA.
• Safe Data Sharing: Enables secure sharing of sensitive data for testing, training, and development.
• Reduced Risk of Data Breaches: Mitigates the impact of potential data breaches by limiting the exposure of sensitive information.

To effectively implement data masking, it's crucial to identify and map sensitive data within your organisation. This allows for targeted masking strategies that balance security and usability.

What is an example of data masking?

Some examples of data masking include:

• Replacing PII data, such as names, addresses, etc. with symbols or characters
• Scrambling the data (although this isn’t as secure). For instance, you could scramble the digits of a National Insurance or Social Security Number, from 3781765 to 8563177.
• Data encryption - converting readable data into an unreadable format, it safeguards sensitive information from unauthorised access.

What are the different types of data masking?

There are a few different types of data masking that organisations can use to protect sensitive data, such as:

1. Static data masking: This involves creating a new copy of the data that is entirely fictitious, in order to keep the original data anonymous. It ensures that the database can be used for non-production purposes.

2. Dynamic data masking: The data is masked in real-time, depending on the users’ permissions. For example, an app user might only be able to see part of the data available, based on whether they’re an admin or not. In this scenario, the original database remains untouched.

3. On-the-fly masking: On-the-fly data masking alters sensitive data in real-time with scrambled characters. Industries such as healthcare and finance can find this useful to work with realistic but protected datasets, and still comply with regulations.

Different companies might choose different methods based on how sensitive the data is, and what it’s being used for.

What are the different techniques used for data masking?

There are numerous techniques used for data masking, but the main ones are:

1. Substitution

‍Swapping out real data for fake data, such as changing customer addresses.

2. Shuffling

‍Randomly shuffling one column of a database so that they don’t match their original records e.g. changing date of births so that they don’t correspond with the correct customer.

3. Date-switching

‍Changing dates by a fixed amount of time (such as 100 days) to ensure real dates aren’t visible.

4. Nulling or blurring

‍Replacing some or all of the characters of a data field with null values or other characters, like asterisks or ‘X’.

5. Lookup substitution

‍Masking a production database with an added lookup table that provides alternative values to the original data, allowing you to use realistic data for testing without overexposure.

6. Encryption

‍Using a cipher to create ciphertext, which can only be read with a decryption key.

7. Tokenisation

‍Replacing sensitive data with unique identifiers called ‘tokens’.

Does data masking comply with GDPR?

In general, data masking can help you comply with GDPR as it stops sensitive data being exposed to those who shouldn’t have access to it. When it’s working correctly, and can’t be traced back to the original data, it can be a great tool to have in your arsenal.

But you should bear in mind that data masking alone won’t be enough to comply fully with GDPR. You should also have measures in place to understand how you’re getting consent for data collection, and what you’re using that data for.

Data Masking vs Data Encryption: What's the Difference?

We’ve gone into detail about what data masking is and what it involves, but let's take a look at another technique in data security: Data encryption.

What is Data Encryption? 

Data masking and data encryption are both fundamental techniques in data security, each serving distinct purposes and providing unique forms of protection. Data encryption transforms data into an unreadable format using encryption algorithms and keys. 

Encrypted data, also known as ciphertext, can only be deciphered back into its original form by users with something called a decryption key. 

Encryption is primarily used to protect data at rest (stored data) and in transit (data being transmitted over networks), ensuring confidentiality and security, even if it’s intercepted by threat actors.

Key Differences

• Purpose: Data masking focuses on protecting data while remaining useful for specific business purposes like testing or training. Encryption, however, emphasises rendering data unreadable to unauthorised users, regardless of its intended use.
• Usability: Masked data retains a semblance of the original information and is reversible in controlled environments. Encrypted data, once transformed, requires a decryption key for access, and is used to secure data throughout its lifecycle.
• Security approach: Data masking relies on obscuring data to prevent unauthorised access while maintaining usability. Encryption employs mathematical algorithms to transform data into an unreadable format, ensuring robust security against unauthorised viewing or interception.

While both data masking and data encryption play crucial roles in data security, their applications differ significantly based on the level of security and usability required by organisations.

What are the challenges of data masking?

As with everything, there are a few limitations that come with data masking, including:

1. Implementation can be a challenge

Time-poor security professionals who are juggling lots of tasks may not implement data masking effectively, leaving it vulnerable to bad actors who can reverse engineer the data to expose the original source.

2. Integration with employees’ work

Disrupting your employees isn’t ideal, and the time it takes to introduce this into the business can be difficult. You might also have to supply additional resources to ensure that business as usual can still continue.

3. Balancing security and efficiency

Too much masking can mean the data you do have becomes unusable. You’ll need to find a balance between having enough security in place that the data is of no use to bad actors, but still usable for your team.

4. Data leakage is still a risk

Data masking is a very effective security tool but it doesn’t entirely get rid of all your problems. As cyberattacks become ever more sophisticated, data leakage of masked data could prove difficult for your business, especially if the hackers know how to unscramble your data. However, masked data will always be more secure than unmasked data.

How can Metomic help you?

Your data security posture should be comprehensive, and take into account all of the places your sensitive data could be stored, including your SaaS apps.

Metomic's data security software uses data masking to ensure that the data we hold for our customers is protected, and secure. Get in touch with our team today to see how Metomic can help secure your sensitive data.