What Is Data Masking: Techniques, Types, Examples, and Best Practices
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What is data masking?
Data masking is a method of replicating a database in which the secret data is modified in such a way that the actual values are no longer accessible.
Let’s read through another definition, to clarify the concept. According to Gartner, data masking is replacing high-value data items with low-value tokens partially or fully.
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The dangers to an organization’s data are numerous and ever-changing, and the implications of a breach may be disastrous. As per the latest IBM security report, the average cost of a data breach rose to $4.24 million in 2021 from $3.86 million in 2020 (a record 9.8% year-over-year increase)
Not to forget, data privacy and security laws like GDPR, make it mandatory for complying organizations to employ data masking techniques to eliminate the risk of data exposure.
Before we get further into the types, techniques, and benefits of data masking, let’s take a look at some examples.
Data Masking Examples
You may protect various forms of data via masking, but the following are some of the most common:
- PII: personally identifiable information
- PHI stands for “protected health information.”
- PCI-DSS: Payment Card Industry Data Security Standard
Here are examples of data masking:
- Changing the order of facts or randomizing sensitive information such as names or account numbers.
- Decoding data to prevent unauthorized people from accessing it without a decryption key.
- Replacement of other characters and symbols of individual details and addresses.
- Changing the data and replacing sections with data from the same dataset.
- Delete sensitive data entries from databases.
Types of Data Masking
Data masking safeguards sensitive information by making it more complicated for a hacker to identify it. The data masking type ensures that data is consistent across several repositories. It constructs a copy of data that backward debugging tools cannot take. Let’s take a closer look at the various types of data masking.
- Dynamic Data Masking
- Static Data Masking
- On-the-fly Data Masking
- Deterministic Data Masking
1. Dynamic Data Masking
Dynamic data masking (DDM) is real-time data masking. DDM is a data transfer limitation in which datasets from processes are changed as they are accessed.
It is generally used to perform role-based safety—for example, customer service and healthcare record management systems. As a result, DDM prohibits the masked data from writing to the production server.
DDM can be implemented with the help of a database gateway. It alters queries sent to the source database and sends the masked data to the requesting party.
2. Static Data Masking
Static data masking procedures can aid in the creation of a clean database copy. All sensitive data is altered until a duplicate can be securely distributed. The steps that static data masking follow are:
- Produce a backup of the database in operation
- Load it to a separate environment
- Remove any redundant data
- Apply masking to the data
After that, the disguised copy can be transferred to the desired location. The actual database and the mock database are kept separate.
- Produce a backup of the database in operation
- Load it to a separate environment
- Remove any redundant data
- Apply masking to the data
After that, the disguised copy can be transferred to the desired location. The actual database and the mock database are kept separate.
Static Data Masking vs Dynamic Data Masking
| Static Data Masking | Dynamic Data Masking |
|---|---|
| SDM is largely utilized in DevOps setups to deliver high-quality data for software design and evaluation. | DDM is generally used in production systems to implement role-based (object-level) authentication to databases or systems. |
| SDM completely substitutes private information. | DDM temporarily hides or replaces sensitive information. |
| SDM is highly secure for production performance. | DDM is less securable than SDM because it is used for role-based requirements. |
3. On-the-fly Data Masking
Before data is stored to a disc, it is masked while moved from production environments to operational systems. Data is retrieved from the original dataset, disguised, and placed into a different database. Only the data that is transferred is masked. The source data is unaffected.
It delivers smaller pieces of masked data. The non-production system can access each group of masked content in the dev/test environment.
On-the-fly data masking is applied when development requires data to be masked without a specific staging environment. This can be due to a lack of space or processes that require the real-time movement of data.
4. Deterministic Data Masking
Deterministic data masking is the process of mapping two data sets with the same type of information so that another always substitutes one item.
For instance, if your database has a first name field that spans numerous records, many tables can have the same first name. If you mask ‘Richard’ to ‘Dan’, it should appear as ‘Dan’ in all connected tables, not just the hidden table.
7 Data Masking Techniques
We have listed seven different data masking techniques that can help conceal your sensitive data.
- Encryption
- Scrambling
- Pseudonymization
- Nulling out or Deleting
- Substitution
- Shuffling
- Data Variance
1. Encryption
One of the most prevalent and effective types of data masking is data encryption. The encryption algorithm converts data into a form that can only be read by the person who has a secret key known as a decryption key.
Encryption is better for data in operation that needs to be restored to its original condition. The data will be secure only if authenticated users access the key. Any unauthorized party can decrypt the data and access the raw data if the decryption keys are compromised. As a result, appropriate encryption key management is critical.
Different encryption algorithms are used for encrypting data. Many ETL tools provide standard encryption function techniques as well. Common algorithms that are used for encryption are,
AES
The AES (Advanced Encryption Standard) algorithm is a symmetrical encryption algorithm. It transforms plain text in 128-bit chunks to ciphertext, utilizing 128, 192, and 256 bits. The AES algorithm is a global standard because it is deemed safe.
MD5
’Message-Digest algorithm 5’ is abbreviated as MD5.
An encrypting or fingerprint mechanism for a document is the MD5 algorithm. MD5 can create a filed thumb impression to verify that a record is similar after a transition. It is used to encrypt DBMS passwords.
Hashing
Hashing is one-way, which means the plaintext is converted into a different hash that cannot be decrypted. Although hashing can be overturned, the power required to decode it makes this impossible.
RSA
The RSA algorithm utilizes asymmetric encryption, employing both a public and a private key. A public key is shared, but a private key is confidential and must not be disclosed to anybody.
2. Scrambling
Scrambling is a simple data masking approach that phrases the characters and integers into a random order, masking the original material. However, this is a simple strategy and can only be used with particular data types. You may prevent confidential or secret data from being revealed in the test suite this way.
When a person with the User Id 832587 in one location undergoes character scrambling, the result will be 388257 in another area. But, anyone who remembers the initial order may know its original order.
3. Pseudonymization
The EU General Data Protection Regulation (GDPR) has created a new term, pseudonymization, to secure personal data. It includes methods such as hashing, encryption, and shuffling.
Pseudonymization is the process that prevents data from being used to identify individuals. It involves removing direct identities and avoiding unknown identifiers that hackers can use to identify a person.
Encryption keys and other information used to recover the old data should be wholly separated and safe.
4. Nulling out or Deleting
By assigning a missing value to a data field, unauthorized users cannot view the actual data. However, it will decrease the integrity of the data and make the testing and development environment harder.
5. Substitution
Substitution is the process of concealing data by replacing it with another value. It is one of the most successful data masking strategies for preserving the data’s unique look and feel. Businesses can use the substitution method with a variety of data types.
For example, using a random search file to cover customer details. It will enable you to use accurate data without revealing the actual testing process. Although this can be tough to install, it is a very effective method of preventing data leaks.
6. Shuffling
Shuffling is identical to a replacement, except it employs the same mask data field for random shuffling.
For example, shuffle the fields of employee names among numerous employee entries. Although the generated data appears accurate, it does not expose personal details. On the other hand, shuffled data is vulnerable to reverse engineering if the shuffling technique is discovered.
7. Data Variance
The data variance approach hides vital financial and transactional data. A variable in data value variance replaces the initial data value. If a buyer buys many things, the data variance will substitute the purchase price with the highest and lowest price paid.
Or, it will mask someone’s payment information by using the discrepancy between their latest and initial sale dates. That is a more manual, low-tech method of practicing data masking, although it can be helpful in a crisis.
Benefits of Data Masking
Cigniti has listed the following few benefits of data masking:
- Reduces the data risks that come with expanding cloud use
- Without revealing data sets, they can exchange data with authorized personnel
- It addresses five categories of threats: data leaks, loss of data, user or security weaknesses, unsecured interfaces, and malicious data usage
- The quality and structural layout of masked data is preserved
Data Masking Best Practices
Some best practices of data masking include:
- Protecting Data Masking Algorithms
- Referential Integrity
- Understanding Data
- Mapping Out Data Scope
1. Protecting Data Masking Algorithms
It’s crucial to think about protecting the data masking algorithms and any other data sources that might be used to scramble the data. These methods should be confidential because only authorized individuals should access actual data. Hackers can decode big chunks of sensitive data to determine which data masking strategies are employed.
Partition of roles is a data masking best practice. IT security staff must establish which unit will use methods and algorithms. But individual algorithm configurations should only be accessed by stakeholders to maintain security.
2. Referential Integrity
It’s impossible to employ a unique data masking technique throughout the entire business. Due to cybersecurity reasons, it may require each business unit to establish its respective data masking.
According to referential integrity, every type of data originating from a business system must be masked using the same algorithm. It will help to protect the whole system from cybercriminals.
When dealing with the same data type, ensure that different data masking techniques and processes are synced across the business. It will make it easier to use data across business divisions in the future.
3. Understanding Data
Before you can safeguard your data, you must first understand your data and distinguish between different security extents. For this reason, cybersecurity and business experts should collaborate to create an extensive list of all collected data within an organization.
4. Mapping Out Data Scope
To correctly use data masking, businesses must first understand:
- What information is considered confidential;
- Who has access to it;
- Which apps use it;
- Where it resides;
While this may appear simple on paper, this procedure may need a significant amount of time due to various processes. It should be scheduled as a distinct project phase. Because if the project scope is not clearly defined, you will end up without gaining any benefit out of it.
FAQs
Why is Data Masked?
When data exits the database’s protection, it is disguised to prevent it from being abused for criminal purposes. The primary goal is to safeguard sensitive business data and defend the rights of the data’s users. It is performed as part of a collection of actions aimed at reducing the danger of security breaches as much as possible and limiting data exposure.
What are the Risks of Masking Data?
Sadly, data can be partially reconstructed from partial masking using un-masking or de-anonymization. Privacy experts have revealed various strategies for identifying individuals and revealing confidential information about them by integrating data from many ‘anonymized’ sources.
Which type of Data can be Masked?
There are several sorts of personal information that can be hidden. These categories are as follows:
- Personal details and birthplace.
- Mother’s forename.
- Education and job history.
- Fingerprint documents or account information.
Is masking a good technique to enable us to use genuine data?
Not just by itself. Data leaking is still a possibility. Several re-identification methods have been devised and shown, allowing poorly disguised data to identify persons and reveal sensitive data about them. Masked data may not be able to identify someone on its own, but it may be combined with other information.
Conclusion
This article has discussed data masking types, techniques, and the best tools. The primary goal of data masking is to generate a functioning replacement that hides the original data. Data masking also protects data privacy while making it more useful.
Data Masking: Related reads
- 11 top data masking tools and software in 2023
- Data obfuscation: Importance, techniques and best practices
- What is data governance: Definition, importance, and components
- 8 data governance best practices from data leaders
- Data Catalog: Does Your Business Really Need One?
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