In a rapidly evolving digital world, data security has become a top priority for organizations of any scale. One important strategy in protecting sensitive information is through techniques data masking.
This article will discuss in depth what data masking is, its purpose, commonly used techniques, and how it differs from data encryption.
What Is Data Masking?
Data masking is the process of altering or disguising sensitive data with characters or surrogate data that has no intrinsic value or intelligence. This method allows the data structure to remain similar or identical to the original data, but the sensitive information can no longer be identified.
Data masking ensures that protected information cannot be accessed by unauthorized parties, while still maintaining the usefulness of the data for testing, training or analysis.
Imagine data masking like replacing someone's face in a photo with an emoji or another image. You can still see that there is someone in the photo, but the real identity cannot be recognized.
The Main Purpose Of Data Masking
The implementation of data masking has several important goals:
1. Regulatory Compliance
Regulations such as the GDPR in Europe, HIPAA in the US, or the PDP act in Indonesia require organizations to protect personal data. data masking helps organizations comply with these compliance requirements by reducing the risk of sensitive data leakage.
2. Protecting Data in a Non-production environment
Developers and QA teams often need realistic data for testing purposes. data masking allows the use of data that is similar in structure to production data without exposing sensitive information.
3. Preventing Internal Threats
Not all employees need access to all sensitive data to do their jobs. Data masking helps restrict access to information based on roles and responsibilities, reducing the risk of threats from within the organization.
4. Supports Secure Data Analysis
Data masking allows organizations to perform data analysis without exposing personal information, facilitating research and development while still maintaining individual privacy.
See also: What Is Audit Sampling? Methods, goals, stages and Case Examples!
Teknik-Teknik Data Masking
There are various methods of data masking that can be applied depending on the specific needs of the organization:
1. Substitution
This technique replaces the original data with false but realistic values. For example, a credit card number is replaced with a number that has the same format but is invalid or untraceable to its owner.
2. RandomizationShuffling)
In randomization, the values in a column are randomized so that they no longer match the original record. These values are still valid and maintain referential integrity, but are no longer connected to the correct data subject.
3. BlurringBlurring)
Blurring changes the numerical value by a random or fixed amount, so that the original value cannot be identified but still retains similar aggregate statistics for analysis.
4. Tokenization
Tokenization replaces sensitive data with tokens or identifiers that have no intrinsic value. The original Data is stored separately in highly secure storage, with a mapping between the token and the original value.
5. Editorial (Masking Out)
This simplest technique replaces characters with symbols such as ‘X’ or ‘*’. For example, the credit card number “4111 2222 3333 4444” becomes “XXXX XXXX XXXX 4444”, retaining only the last digit.
6. Nuisance Data
This method adds or modifies additional data to make the original information difficult to identify. For example, adding false data into a dataset to obscure patterns that might identify individuals.
Difference between Data Masking and data encryption
Although both are used to protect data, data masking and data encryption have fundamental differences in their purpose and implementation:
| Aspect | Data Masking | Data Encryption |
|---|---|---|
| Purpose | Permanently hide original data from unauthorized users | Stores data in an encrypted form that can be restored to the original form |
| Reversibility | Usually irreversible (not refundable) | Reversible (can be restored with encryption key) |
| Usage | Suitable for testing, training and static data | Suitable for data in transit and storage |
| Key Requirement | No need for cryptographic keys | Requires a key for encryption and decryption |
| Risks | Relatively low because it can not be returned | If the key leaks, data could be exposed |
In other words, data masking is the ideal solution for non-production environments, while encryption is more suitable for data protection in storage or when transmitted.
When To Use Data Masking?
Data masking should be used under the following conditions:
- When using data for system testing purposes
- In employee training with mock data
- For business analytics with non-productive data
- When sharing data with third parties or vendors
- In the process of data migration
Use encryption when data must remain secure but can be accessed again by legitimate parties, such as communication between servers or storage of important files.
Often times, companies combine these two techniques in a more comprehensive data security strategy.
Conclusion
Data masking is an important solution in modern data security strategies. With the aim of protecting sensitive data from unauthorized access, data masking allows companies to run various internal processes such as testing and training without exposing the original data.
Compared with encryption, data masking is irreversible and more suitable for use in non-productive environments. Meanwhile, encryption remains the top choice for maintaining data confidentiality in communication and storage.
Understanding the differences and implementing them will help organizations design data security policies that are effective and compliant with applicable regulations. In the midst of increasing cyber threats, a smart and layered approach to protecting data is not just a necessity, but a vital necessity.
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