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Data Security in Cloud Computing: 8 Key Concepts

Migrating to a cloud computing platform means your responsibility for data security goes up considerably. Data with various levels of sensitivity is moving out of the confines of your firewall. You no longer have control – your data could reside anywhere in the world, depending on which cloud company you use. Moving to the public cloud or using a hybrid cloud means the potential for cloud security issues is everywhere along the chain. It can happen as the data is prepped for migration, during migration, or potentially within the cloud after the data arrives. And you need to be prepared to address this every step of the way. Get a free trial of Qualys’ top-rated cloud security platform for finding and patching vulnerabilities across the cloud, on premises and mobile devices. Data security has been incumbent on the cloud service providers, and they have risen to the occasion. No matter which platform you select in the debate between AWS vs. Azure vs. Google , all sport various compliances to standards like HIPAA, ISO, PCI DSS, and SOC. However, just because the providers offer compliance doesn’t give customers the right to abdicate their responsibilities. They have some measure of responsibility as well, which creates a significant cloud computing challenge . So here are eight critical concepts for data security in the cloud. Privacy Protection Your data should be protected from unauthorized access regardless of your cloud decisions, which includes data encryption and controlling who sees and can access what. There may also situations where you want to make data available to certain personnel under certain circumstances. For example, developers need live data for testing apps but they don’t necessarily need to see the data, so you would use a redaction solution. Oracle, for example, has a Data Redact tool for its databases. The first step is something you should have done already: identify the sensitive data types and define them. Discover where the sensitive data resides, classify and define the data types, and create policies based on where the data is and which data types can go into the cloud and which cannot. Too many early adopters of the cloud rushed to move all their data there, only to realize it needed to be kept on-premises in a private cloud . There are automated tools to help discover and identify an organization’s sensitive data and where it resides. Amazon Web Services has Macie while Microsoft Azure has Azure Information Protection (AIP) to classify data by applying labels. Third party tools include Tableau, Fivetran, Logikcull, and Looker. Preserve Data Integrity Data integrity can be defined as protecting data from unauthorized modification or deletion. This is easy in a single database, because there is only one way in or out of the database, which you can control. But in the cloud, especially a multicloud environment, it gets tricky. Because of the large number of data sources and means to access, authorization becomes crucial in assuring that only authorized entities can interact with […]

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