The rapid growth of data collection, security threats, and regulatory requirements has transformed the idea of "data governance." What was previously a spirited conversation among compliance teams and lawyers has turned into a mainstream business challenges. It’s now a strategic priority to apply and enforce data governance standards for any organization, not just the traditional regulated industries like finance and healthcare. And the way to make sure data stays private at scale is through automated data governance.
Creating value from your data requires sharing it throughout your company. This includes setting policies to stipulate who can access which data, while complying with relevant regulations. But that’s where traditional data governance stops. Although it’s called “data governance” by some, this narrow definition of data discovery, inventory, and classification rarely extends to what the average person would think of as “governing”: access control, policy enforcement, and data security. Stopping before real governance can leave sensitive data exposed. Data discovery and classification are critical first steps of a governance strategy but now that you found and classified your data, it’s time to control and protect it. The best way to do this is through automated governance.
Integrating a data catalog with an automated policy enforcement engine allows you to achieve automated data governance. By automating governance you'll create policies that are quick to implement and enforce, access controls that focus on sensitive data, and detailed records of every data request and usage. Data governance must be easy to implement and scale for companies as part of their responsibility to collect, store, and protect sensitive data. Bridging the gap between security and access can help you stay in compliance with applicable regulations worldwide while ensuring protection for the most valuable assets.
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