3 Essential Principles for Protection of Data Integration

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When it comes to data sharing, organizations must take into account the implications of security and compliance. To help ensure that this is done, this article will discuss three core principles for securely integrating data.

No-code and low-code tools for moving, sharing and analyzing data, such as ETL and ELT platforms, iPaaS platforms, data visualization apps, and databases as a service, have enabled organizations to easily handle self-service integration tasks. The corresponding increase in SaaS apps used in businesses today only increases the need for self-serve integrations.

However, these apps often contain sensitive customer data, payroll information, and other information that must be kept secure. Once you take data out of its controlled environment, there is a risk of access control misalignment.

Organizations can minimize the risks of data breaches by separating data storage, processing and visualization functions. Doing this will provide a secure environment for data scientists to work in and the ability to clearly define what data should be used in various tasks.

Organizations should also remember to exclude data when it is not necessary to access it. Additionally, any sensitive information that needs to be shared should be masked or hashed for privacy and security reasons.

Finally, having systems in place for tracking and logging data access can help organizations remain compliant with regulations, as well as allow them to quickly identify and react to any suspicious behavior. Following these measures will help organizations securely integrate data and minimize potential security incidents.

The company mentioned in the article is an ecommerce company, who needs to clearly define what data needs to be analyzed and employ data replication techniques to keep that data secure.

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The person mentioned in the article is a data scientist looking for access to datasets to analyze how the weather impacts the ordering process or what the most popular item is in a specific product category. This data scientist also needs to be given a secure, separate sandbox environment so as to not risk exposing any sensitive data.

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