Data discovery to support data governance
1. Data discovery to support data governance
In this video, we’ll explore data discovery. We’ll consider how data discovery relates to data governance and data sovereignty, and how data discovery works to keep sensitive data from being moved out of protective spaces. Let’s get started! Data discovery is the process of searching, identifying, and analyzing large amounts of data within an organization to uncover hidden patterns, relationships, and insights. Data discovery can identify risks and inform better decision making. For organizations, data discovery supports both data governance and data sovereignty. During data discovery, organizations can flag data that is subject to governmental sovereignty laws and regulations. Then, they can use data governance to ensure they’re in compliance with those regulations. Data transparency enables organizations to remain in compliance with data sovereignty laws and regulations. Organizations use data discovery to check for the dimensions of data quality, including the accuracy, completeness, consistency, timeliness, validity, and uniqueness of data. Each of these dimensions factor into how organizations use data for different purposes, but completeness, timeliness, and accuracy especially affect the ways organizations ensure transparency. Transparency is one benefit of data discovery. In data discovery, an organization’s data may come from many different data sources and be stored by cloud service providers. The organization needs to keep track of this data and know how the data is collected, where it is collected from, and where it’s stored. Data transparency ensures that data is available and searchable. For security, discovery of sensitive data is particularly important. Sensitive data discovery is finding and identifying all confidential and regulated information kept by an organization. This includes any data that’s regulated by law, like confidential payment data and PHI. It also includes finding data related to intellectual property. For sensitive data, it's important to know what kind of data it is and where it’s located to ensure that it’s properly protected. As a cloud security professional, there are tools that you can use to find and protect sensitive data. One tool that we’ll explore is a data loss prevention engine. At various stages of the data lifecycle, you can use a data loss prevention engine -also known as a DLP engine- to filter and search for sensitive data. You can also use DLP to inspect, mask, or remove sensitive data once it’s discovered. Doing so may lower the risks inherent to certain data types. The DLP can also be utilized to complement encryption for data at rest, manage access control, and other security measures. You can also examine data closely with DLP. After, you might decide to apply security controls based on the data’s sensitivity, or move the data to a less restricted area. If you move data, during a cloud migration for example, you can use data discovery to validate whether the data is being moved into places that are properly secured. Data discovery also checks to make sure there are no mistakes in data classification and placement. This prevents regulated data from being moved into an environment without proper controls in place. Now that you know about data discovery -and how data discovery supports data governance and data sovereignty- you have the info you need to identify and protect sensitive data wherever it is.2. Let's practice!
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