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Uncovering risks in your dark data

Supreeth Rao

Data discovery is a foundational component of any robust data security practice. Organizations must find and manage all their data (sensitive or not) because you cannot protect what you can’t see.

However, organizations struggle with dark data in the cloud - data that they are unaware of and possibly growing exponentially without the oversight of security and risk teams. Dark data typically tends to be unstructured and can contain sensitive information such as credit card numbers, medical beneficiary numbers, and developer secrets.

Legacy data loss prevention (DLP) tools require manual onboarding and do not understand data within stores, thereby not effective with growing cloud data stores. More importantly, they are not architected to secure data stored in cloud environments such as AWS and Snowflake. 

Organizations need a cloud data protection platform that uses machine learning (ML) to find sensitive information in unstructured data in AWS and Snowflake. Classification requirements vary across industries and organizations. It is highly valuable if a healthcare organization or financial institution can use its unique data taxonomy to expand the ML capabilities and customize the sensitivity of the discovered data types.

Theom is a data-centric cloud data protection platform. It maps cloud data environments and identifies sensitive data to be protected, including dark data. It pinpoints risks, including the impact on the  business from cloud security risks. It remediates these risks enabling enterprises to implement cloud data protection. Theom fosters tighter collaboration between data and security teams and spurs them to protect enterprise data proactively.