How to Choose the Right Data Masking Tool for Your Organization
As data privacy and security become increasingly important in today's digital landscape, selecting a robust data masking tool is crucial in protecting sensitive information.
Data masking, as we know, obscures sensitive information by replacing it with realistic but fake values, making it suitable for use in testing, demonstrations, or analytics.
It preserves the structure of the original data while altering its values through sophisticated algorithms, making it impossible to reverse-engineer the masked data.
As data privacy and security become increasingly important in today's digital landscape, selecting a robust data masking tool is crucial in protecting sensitive information. With the abundance of available options, choosing the right one that fits your needs and requirements can be overwhelming.
Before finalizing a data masking tool, let’s understand the key differentiators.
The Immediate Checklist to Follow
· Data Types: Ensure the tool supports the data types you want to mask, such as structured, semi-structured, or unstructured data.
· Masking Techniques: Evaluate the available data masking techniques and determine which ones meet your requirements, such as character substitution, redaction, or shuffling.
· Performance: Consider the tool's performance, such as the speed and scalability of the masking process, especially if you are dealing with large data sets.
· Integration: Check if the tool integrates with your existing data management tools, such as databases, data warehouses, or cloud platforms.
· Security: Ensure the tool provides adequate security and encryption measures to protect your masked data.
· Cost: Consider the total cost of ownership, including licensing fees, maintenance, and support.
· User-friendly: Ensure the tool is easy to use and has a user-friendly interface.
· Technical Support: Evaluate the level of technical support the vendor provides and ensure that it meets your needs.
Furthermore, evaluate the tool based on the following criteria:
Data Product Approach
Adopting a data product approach to data masking streamlines the implementation process, reducing time and costs while accommodating enterprise complexities. The tools ingest data from various sources, mask sensitive information, and deliver compliant data to authorized consumers, all while ensuring that each business entity's data is handled correctly. This approach simplifies the data masking process, making it more efficient and cost-effective.
Data products can make data accessible to broader stakeholders, leading to better collaboration and knowledge sharing. Moreover, the enable can help organizations monetize their data assets, turning data into a valuable resource. Data products can make data accessible to broader stakeholders, leading to better collaboration and knowledge sharing.
Look for a tool that embraces a contemporary data management lifecycle implementation approach. Of late, many tools have improvised their capability to match the high volumes of data in transit and comply with the regulations. If you'd like a micro-database approach, just look at the K2view data masking solution. Their data masking tool provides static and dynamic masking capabilities, making it a versatile solution for test data management and customer 360 use cases. The platform offers a unified implementation for both types of masking.
It protects sensitive data by anonymizing individual entities such as customers, orders, and devices in real time. Personally identifiable information is never at risk, and the relationships between the masked data remain intact.
Automated Sensitive Data Discovery
Since enterprise data continuously evolves, testers will require updated, qualitative, regulatory-compliant data. This requires a real test environment, making masking a permanent function.
Data discovery and masking must both be quick and automated to avoid the manual effort of searching for sensitive fields among all your data.
The correct data masking tool would enable automated discovery, ensuring faster compliance and updating masked data.
The global sensitive data discovery market is expected to grow from USD 5.1 billion in 2020 to USD 12.4 billion in 2026. This is a CAGR of 16.1%.
However, most of these solutions are expensive and often deprive the SME of must-have integrations. Look for a masking solution with built-in data discovery capabilities that assigns the right masking algorithm to keep the data sets safe.
One example would be the Imperva Data Security Fabric (DSF). The fabric offers a comprehensive solution for organizations to gain visibility and control over all their data, whether structured, semi-structured, or unstructured, regardless of location. Its unified agent and agentless architecture facilitate this by providing a single platform to manage all data repositories.
In the pursuit of further strengthening PII protection, GDPR introduced pseudonymization. This technique disables the use of data for personal identification. It lays down removing direct identifiers or avoiding multiple identifiers that could identify a person upon combining.
The pseudonymization technique replaces PII with a pseudonymous identifier. Since sensitive data about individuals is always at risk of exposure, a randomized identifier helps mask the data set. These sets are helpful for research and analytics purposes without compromising data privacy and protection.
In combination with encryption and access control techniques, pseudonymization elevates the security of sensitive data. It also enables re-identifying individuals for several use cases, such as legal investigations.
Not to miss, encryption keys or any form of data that could revert to original values should be stored separately.
The data management platform should provide end-to-end support for pseudonymization. Given the strict stance of data masking regulations, such as GDPR and others, this is undeniably one of the major prerequisites.
While there are many key metrics to consider, as discussed in the beginning, the major three differentiators should land you with the most appropriate tool. Masking is a continuous process and requires a platform that provides uninterrupted streaming of masked, updated and qualitative data.
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