Data is the new oil, and its volume and value are growing every moment. However, with great value comes great responsibility, especially when it comes to protecting consumer confidentiality.
Exactly why enterprises across the sectors are implementing contemporary techniques to minimise the risks of breaching regulatory guidelines such as the GDPR, this demands proactive steps, and data masking is the first step for most new entrants. As of today, data masking’s market adoption is in the right direction attracting more enterprises to implement the technique.
Most likely, by 2029, the product market could value anywhere around USD 1.49 billion, which is a CAGR of 12.85% starting in 2021.
In this regard, K2View, a leading data management platform, provides a comprehensive data masking solution that enables organizations to efficiently identify and mask sensitive data within their databases, ensuring compliance with regulatory standards and reducing the risk of data breaches.
How to Perform Data Masking Using K2View?
The popular entity-based approach using micro-databases follows the standard steps with additional security and automation. This fastens the process, minimizes errors and ensures qualitative output. Here’s a quick breakdown.
#1 Identify sensitive data entities
In the first step, the K2View masking solution narrows down the most sensitive data entries needed for masking. In addition to personally identifiable information (PII) such as name, age, address, and location, the system covers financial data, health records and others. This step is also important to ensure that only targeted data sets are masked with a vision to protect information from unauthorised access.
#2 Define the masking rules for each entity
After identifying the sensitive data for masking, the K2View system defines the masking rules for every entity. This involves identifying which fields within each entity contain sensitive information that needs to be masked and deciding on the appropriate masking technique to be applied to each field. Masking techniques could include techniques such as data obfuscation, encryption, or tokenization.
#3 Creating a masking model
As we know, a masking model is a set of rules that defines how sensitive data within an entity should be masked. It provides a framework for applying the defined masking rules to the data fields within the entity, ensuring that sensitive data is appropriately masked.
Creating a masking model involves identifying the data fields within each entity that contain sensitive data and defining the masking rules that should be applied to each data field.
K2View simplifies the creation of masking models, including a user-friendly graphical interface and a rule-based engine that can be used to define masking rules for each data field. Post-creation, the masking models can be mapped to the corresponding data sources to begin the masking process.
#4 Mapping the entities to data sources
At this step, K2View ensures that the masking model maps to the correct data and that sensitive data fields are properly masked. Mapping involves identifying the location of the data within the data sources and linking the data sources to the relevant entity in the masking model.
This process enables K2View to identify which data fields within the data sources need to be masked and how they should be masked according to the defined rules.
Accurate mapping is crucial to ensure that the masking process is effective and that sensitive data is protected. K2View automates the mapping process, which helps to streamline the overall data masking process.
#5 Apply the masking model to the data
Once the entities have been mapped to the data sources, the final step is to apply the masking model to the data to mask the sensitive data fields according to the specified rules. This step involves using K2View’s masking engine to apply the masking rules to the data, which can be done in real-time, on-the-fly, or through batch processing. Once the data has been masked, it can be used safely and securely for its intended purpose.
#6 Testing & Deployment
Test the masking to ensure that the data has been masked correctly. Once the data has been masked and tested, it can be deployed for use in applications or other data processing operations. Furthermore, continuously monitor the masked data to ensure that the masking rules are still valid and that the data is being protected.
The K2View Differentiator – In-Flight Masking and More
K2View’s data masking solution also includes options for masking data on-the-fly, masking data based on user roles, and masking data in real-time, ensuring that sensitive data is always protected.
K2View’s dynamic data masking capability allows for the real-time de-identification of sensitive data based on user roles, privileges, and locations. With hundreds of out-of-the-box masking functions, such as substitution, scrambling, randomizing, and nulling-out, and the ability to create custom anonymization functions without coding knowledge, K2View offers a powerful and flexible solution.
The unstructured data masking feature protects images, PDFs, and text files and maintains referential context across structured and unstructured data for consistent and accurate results. K2View integrates with any data source, technology, or vendor, ensuring referential integrity of the masked data with its patented entity-based masking approach.
One of the key advantages of K2View’s solution is its auto-discovery mechanism, which scans data sources, identifies sensitive data and masks it according to predefined rules. With the integrated data catalog, users can classify and map sensitive data, search metadata and database content on a granular level, and apply data masking functions to achieve full regulatory compliance.
As data continues to play an increasingly pivotal role in business operations, safeguarding sensitive information will become even more critical in the years to come. With K2View’s advanced data masking capabilities, organizations can ensure that they remain ahead of the curve in terms of data protection while also gaining valuable insights from their non-production data. By embracing the latest data management tools and techniques, businesses can unlock new opportunities for growth and innovation while safeguarding their most valuable asset: their data.