Data masking is an important aspect of data security. It is meant to protect sensitive information from cyber threats while also maintaining the usability of data for testing, development, and analytics. Over the years traditional data masking methods have been used. However, the traditional methods are now being challenged by the recent emergence of AI powered tools. 

This article will explore the differences, benefits, and limitations of traditional data masking methods in comparison with AI-powered Data Masking tools.

What are the traditional data masking methods?

Traditional data masking works by replacing sensitive data with fictional data. However, it is ensured that such fictional data appears realistic. The primary techniques include:

  1. Static Data Masking (SDM)

SDM method permanently replaces sensitive data in a non-production environment. It is done by extracting a copy of the database, masking sensitive fields, and then using the masked data in testing and development. This method ensures that the data is irreversibly masked thereby enhancing security.

However, its major drawback is that the process is time-consuming and resource intensive. Further, it is difficult to keep the masked data synchronized with production data.

2. Dynamic Data Masking (DDM)

DDM method works by masking data as and when it is queried from the database. Therefore, the process applies masking rules in real-time as users access the data, without altering the original data. This provides real-time data protection with the need to duplicate the database. However, this process may impact performance, speed, and is a little complicated to implement and manage.

3. Data substitution

Data substitution method replaces sensitive data such as customer names and credit card numbers with non-sensitive equivalents. The non-sensitive equivalents are identified from a pre-defined set via lookup tables or algorithms to substitute data consistently. The main benefit of this method is that it maintains referential integrity and a realistic data format. However, this may again require an extensive setup which involves management of substitution sets.

4. Data shuffling

As the name suggests, data shuffling method randomly shuffles data within a certain column. This ensures that the data remains obscured but also appears realistic. This process is quite simple to implement and effective for specific types of data. However, where highly sensitive data is required, other methods with better security should be preferred.

What are the AI data masking tools?

AI data masking tools use machine learning and artificial intelligence to give a powerful boost of efficiency to the data masking process. Some of the key characteristics that AI brings to the table are:

  1. Automated pattern recognition

AI algorithms are well capable of identifying and classifying sensitive data based on patterns and context. This reduces any manual intervention and errors while handling complex data sets. However, creating such algorithms requires robust training data and continuous learning.

2. Contextual masking

AI tools can be programmed to use masking techniques that consider the context and relationships behind the data. Such contextual accuracy helps maintain and preserve the utility of the data for analytics and testing. For example, consider patient data maintained by a hospital. To mask such data while maintaining contextual accuracy, the hospital may replace the data with fictitious data within the same gender, ethnicity, and age range. However, this requires high computational capacity, which may be infeasible and too complex for small-scale operations.

3. Adaptive learning

AI tools can adapt continuously and learn about new data types and patterns. This helps such AI tools to keep with the pace of evolving data environments.

4. Scalability and performance

AI tools can handle large volumes of data with higher efficiency. This feature makes AI tools suitable for large-scale data environments and real-time applications. However, the user must have enough computational capacity to setup such a system.

Comparative analysis

It is clear that AI Data Masking methods are miles ahead of traditional tools. Let’s compare both on some of the key parameters relevant for users: 

On the parameter of accuracy and consistency, AI tools ensure consistent and accurate masking, and adapt to any changes in data patterns. On the other hand, traditional methods are reliant on predefined rules. These rules require manual setups where inconsistencies may creep in. Further, the system may not always keep pace with a dynamic data environment. 

Again, the implementation and maintenance differ between traditional and AI tools. Traditional methods are more often than not labour-intensive and require constant updates and manual intervention. On the contrary, AI tools offer automated processes. This reduces the need for continuous manual oversight. Having said that, setting up an AI based solution may be complex and consume higher computational capacity. They also require intense initial training and configuration.

On grounds of performance and impact, the verdict is quite straightforward. Traditional methods are slow, particularly in dynamic scenarios and they require constant intervention. AI tools, on the other hand, are efficient and scalable. They are well capable of handling real-time data masking without significant performance degradation and further maintain accuracy. 

Lastly, the most important criteria are security and compliance. While over the years traditional methods have kept up with the needs of regulatory compliance, they may struggle with complex data environments of the modern age. AI tools, on the other hand, provide robust security features and adaptability. This makes them well-suited for stringent compliance requirements for complex databases.


Both traditional data masking methods and AI data masking tools have their merits and drawbacks. While traditional data masking methods have proven to be effective over the years, they are cumbersome and less adaptable in complex data environments. In contrast, AI data masking tools come with advanced capabilities. These include higher efficiency and adaptability which make them ideal for modern, large-scale data operations that keep evolving on a day-to-day basis. The decision to opt for one method over another should only be made after assessing specific needs of an organisation, resources, and data environments.

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