Organizations that collect and store sensitive data, such as governments, hospitals, and financial institutions, face serious security and data privacy risks. Fortunately, data masking provides a solution to protect this data from unauthorized access while enabling its use for testing and development. With data masking, businesses can safely share sensitive data with authorized parties while maintaining compliance.
This isn’t a theoretical challenge; ransomware attacks are on the rise. Failing to protect against cybercrime and data breaches can damage an organization’s reputation and trust with clients. It can also lead to significant legal and regulatory penalties. Businesses need to take every possible precaution to avoid exposing sensitive data while maintaining their operations.
However, implementing data masking can present certain challenges, such as sensitive data search and identification, resource-intensive masking, and maintaining data integrity. This article will explore the top five data masking challenges and how to overcome them with the Accelario platform.
Data Masking Challenges
- Challenge 1: Sensitive Data Search and Identification
Organizations in regulated industries must ensure that data is secure and compliant. Data should be analyzed to determine if it falls under specific privacy regulations, which can be time-consuming and computationally intensive without proper solutions.
Solution: Accelario’s advanced search, which is based in data science, quickly and accurately identifies sensitive data within databases. The search algorithms go beyond simple column name approaches or even string patterns, leveraging data science and artificial intelligence to flag sensitive data that might otherwise be missed by legacy search algorithms.
- Challenge 2: Performance
Data masking can be resource-intensive and significantly impact system performance, requiring additional hardware or software investments.
Solution: Accelario’s masking module leverages parallel processing and is fine-tuned to use resources based on capacity, cost, and time. This makes it five to 10 times faster than other approaches.
- Challenge 3: Scalability
Generally, organizations that use data masking are challenged by the size of their own operations. The daily flow of business is constantly producing new records and building out massive data sets. Data masking solutions must handle large databases.
Solution: Accelario’s masking is designed for scale and can handle terabytes of data successfully. It is one of the most robust solutions on the market.
- Challenge 4: Maintaining Data Integrity
Data masking solutions need to maintain the integrity of the data across tables and databases. It can be difficult to ensure that the data remains consistent and accurate after it has been masked because primary and foreign key-based relationships may accidentally get severed, rendering the test database useless.
Solution: Accelario ensures data integrity by masking data consistently, understanding relational integrity, and applying special treatment to primary and foreign keys.
- Challenge 5: Repetition
Data masking is a repetitive process. Those tasked with data masking may need to spend a lot of time and effort to effectively accomplish their goals.
Solution: Accelario’s masking process is fully automated and can be integrated into a customer’s continuous integration or continuous delivery framework through RESTful APIs and plug-ins. This reduces the workload of overworked data managers.
While cyber risks cannot be eliminated, organizations can take critical steps to prevent sensitive data from falling into the wrong hands. Data masking provides a powerful solution for protecting data, increasing efficiency, and driving business forward.
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