Protecting Sensitive Data in Hadoop
Hadoop is a unique architecture designed to enable organizations to gain new analytic insights and operational efficiencies through the use of multiple standard, low-cost, high-speed, parallel processing nodes operating on very large sets of data. The resulting flexibility, performance and scalability are unprecedented. But data security was not the primary design goal.
Major Provider of Insurance, Banking and Travel Products and Services
A leading provider of insurance, banking and travel products and services with over nine million customers in 11 states needed to secure terabytes of customer and corporate data.
Data-Centric Protection Service Offering
The Cost of a single successful cyber-security attack exploit is Significant and the Cost of Prevention is 1/10 the cost of an exploit.
Use Case: Data De-Identification
Neutralize data breaches: a new breed of cyber attacks—advanced malware, exploitation networks, and motivated insiders—are stealing sensitive data from vulnerable ecosystems. But companies can neutralize data breaches by rendering the data valueless, de-identifying data through encryption, tokenization, and data masking.
Advancing Medical Research by Sharing Big Data in the US Government
By 2017, Big Data Technology will be the norm for data management.
Enough is Enough: Fight Back Against Cybercrime
The recently-publicized data breaches are indeed a wake-up call. While the risks have been there all along and baseline control programs have largely been in place, it is now clear that we must do more to protect our customers, our systems and our companies.
Big Data, Meet Enterprise Security
Large organizations worldwide are working to develop and deploy Big Data analytical facilities alongside their established business intelligence infrastructure. These initiatives are motivated in nearly equal parts by the conviction that new business insights and opportunities are buried in the avalanche of new data, by the knowledge that conventional business intelligence systems are unequal to the task, and by the fear that competitors will be first to master and exploit the available new data streams.
