The world is radically changing, with data at the center of value creation for the modern enterprise. Smart devices stream massive volumes of data that organizations leverage for customer and competitive insights. And those are not just mobile phones anymore, Gartner estimates that by 2020 there will be 20 billion connected devices of all kinds — from connected cars to refrigerators and cars — generating and streaming rich data across the web. That is one of the main trends contributing to the doubling of the digital universe every two years. It is a golden opportunity for enterprises, but not without potential risks.
The biggest risk in big data is that among the petabytes of data captured for analytics, there are massive amounts of sensitive information. Sensitive data captured by enterprises includes personal information, intellectual property, health information, financial data, and more new classes of data than ever before. Even data not apparently sensitive at face value could be combined with other disparate pieces of data to reveal personally identifiable information, and if stolen, be used for fraud and trigger penalties due to privacy legislation.
This risk exposure makes big data the number one priority for increased security spending by Chief Security Officers. This increased security scrutiny, however, is having unintended consequences. Traditional IT security tools were not designed to protect big data when being use by data scientists and analysts. The result is that more often than not, access to big data is limited to just a few employees in order to prevent a data breach. This ultimately hurts a company’s ability to gain insights.
The challenge is that as data flows throughout the enterprise from the edge of the network where it is created, through thousands of applications and systems, all the way to storage in the cloud or on-premises systems, data must be protected—at-rest, in-motion, and in-use. But traditional system and perimeter IT security can’t achieve this, except in silos, leaving security gaps in the modern hybrid enterprise:
- Data in the cloud is vulnerable—Traditional on-premises security controls cannot be extended to the hybrid cloud nor protect data streaming from IoT devices.
- Data protection can’t scale dynamically with Hadoop—Traditional system-based security cannot scale at the speed of big data growth for most organizations.
- Data is not protected while in use—Traditional data storage security cannot protect data when it is being used by applications or being analyzed.
- Data is vulnerable at third-parties—On-premises embedded system security cannot extend when shared outside the organization.
Micro Focus has a new security approach to protect big data, embedding security into the data itself. Through Voltage SecureData, organizations can:
- Protect data at-rest, in-motion, or in-use with a security policy that travels with the data
- Protect data in Hadoop data lakes and across Hybrid IT
- Enable analytics in the protected data, without compromising security
- Neutralize data breaches by keeping data de-identified at all times
Data security and encryption