By Crystal Woody, Senior Director of Strategic Communications at Privitar
Not too long ago, we were talking about how software was eating the world (credit to Marc Andressen for that phrase). Well, all that software created data, and that data is moving to the cloud. Organizations of all sizes are adopting modern data architectures to democratize data for analytics and machine learning (ML), and increasingly turning to the cloud to store, catalog, manage, and analyze that data. The cloud offers on-demand scalability, powerful cloud analytics, and machine learning technologies to innovate and accelerate data-driven insights. So it’s definitely time to think about cloud data privacy. Here’s what you need to know to get started.
Centralizing data in the cloud enables easier data access, but it also increases the privacy risks and multiplies the points of exposure if (or when) there’s a data breach. Broad new privacy regulations, including the European Union’s GDPR, California’s CCPA, and Brazil’s LGPD, place strong restrictions on the use of sensitive data; it can’t be leveraged for analytical, artificial intelligence (AI), and ML services. As a result, the value from data often goes unrealized because personal or sensitive data assets cannot be easily, broadly, and efficiently accessed by business analysts and data scientists.
But safe, usable data is an incredibly valuable asset for nearly every organization. It can be leveraged to gain valuable insights, support data-driven decisions, and fuel innovation by identifying and capitalizing on new revenue opportunities and enabling organizations to build personalized customer experiences. So how can organizations scale their use of data in the cloud? Essentially, they must leverage automated data privacy tools to ensure data privacy is built into the architecture by design.
Here are five key considerations to making sure your data stays safe, accessible, and usable in the cloud.
Embrace both data security and data privacy strategies.
GDPR and other data protection regulations set out a number of requirements, and you’ll face significant fines if you fail to comply.
Data privacy is a key part of your cloud strategy, and you should embed into your system design process.
Automated data provisioning with privacy protections built in accelerates data access.
Data is safest when it is de-identified, so learn how to minimize the risk of identifying individuals in data sets.
We create a vast amount of data every day, and much of it is already on or moving to the cloud. Data is stored, catalogued, managed, and analyzed in the cloud already, and data security is simply not enough to protect sensitive information. Make sure that you’re prioritizing cloud data privacy from the start by building it in from the beginning and automating data provisioning and de-identification.
Learn how you can quickly and safely use your sensitive data in the cloud. Read the Cloud Data Privacy 101 Guide.