Data masking has emerged as a crucial solution in the digital landscape, especially in 2023, where data stands as the most profound and vulnerable digital asset for businesses. Elijah Mokin, Head of R&D at Symfa, delves into data masking and its potential.
Data masking is a technique that creates a fake version of an organisation’s data with a similar structure but different content. It plays a crucial role in scenarios like software testing or user training, where using real data is unnecessary. For instance, in the case of a bug during data analysis, a masked version of the production data allows developers to troubleshoot without compromising sensitive information.
Two common applications of data masking include outsourcing ETL or data cleansing jobs to a third party and ensuring API usage security. In both cases, data masking allows real-time data access while concealing confidential information, fostering trust in testing results and enhancing security.
A common misconception surrounding data masking is that it is just another term for encryption. However, While encrypted data can be unlocked with the right key, masked data produces a fake version with no appeal to hackers. For example, PII columns like full name and social security number may need masking, and the extent depends on the business and data analysts’ decision.
Implementing data masking requires skilled data analysts and automated tools. The automation of data masking is crucial to efficiency, especially when dealing with large databases and backups. Finding sharp, quick-learning talents or data scientists with the right background is essential.
What are the compliance risks I face if I skip data masking?
Skipping data masking may expose businesses to compliance risks, especially concerning data protection regulations like CCPA, GDPR, HIPAA, and PCI DSS. Increased penalties for data breaches and heightened liabilities are likely consequences. The real risks involve legal consequences and reputational damage, which could be severe for smaller businesses.
To address this issue effectively, businesses are advised to research suitable data masking tools, explore projects for implementation, and identify candidates with expertise in handling sensitive data.
Leveraging tools within platforms like Microsoft Azure or AWS DMS can streamline the process. Additionally, considering outsourcing with vendors experienced in finance, insurance, or banking-focused data handling can be a game-changing strategic move.
Overall, data masking stands as a valuable technique in data security management, offering a layer of protection beyond firewalls. As businesses navigate the evolving landscape of data security solutions, implementing data masking can be a crucial step.
Keep up with all the latest FinTech news here.
Copyright © 2023 FinTech Global
