Leading companies in nearly every industry are using data as an essential driver of competitive advantage. As such, Big Data projects are hungry for data, and increasingly these projects involve data which is personally identifiable or otherwise considered sensitive.
The task of protecting this sensitive data is often dictated by balancing two traditionally opposing factors: the risk of potential threats versus the benefit of accessing and using the data. As organizations leverage Big Data platforms like Hadoop to analyze much larger, more diverse data sets, the challenge of effectively securing sensitive data while maintaining usability becomes increasingly difficult.
Going well beyond traditional and limited access control methods available in HDFS, today’s best practices for protecting Big Data dictate securing data at the moment of ingestion, so that it remains protected throughout the cluster, at rest, in flight, and in use.
Find out more in our infographic series, The 7 Pitfalls of Implementing Silo-based Data Security.
To view or download a PDF version of the fourth installment, “Big Data Security Beyond Just HDFS,” click here.