According to Oracle’s Neil Mendelson, many companies today make a key mistake in setting up their big data environments. “In an effort to gain insights and drive business growth, companies can too often overlook or underestimate the challenge of securing information in a new and unfamiliar environment,” says Mendelson, vice president for big data and advanced analytics at Oracle. That lack of attention to big data security requirements can, of course, leave the organization open to attacks from any number of unknown sources.
Other evolving circumstances also contribute to a wide range of security-related risks, hurdles, and potential pitfalls associated with big data. As the Cloud Security Alliance, an industry group, notes: “Large-scale cloud infrastructures, diversity of data sources and formats, the streaming nature of data acquisition, and high-volume inter-cloud migration all create unique security vulnerabilities.”
Two additional complicating factors include:
• Outdated approaches. Previous perimeter-based approaches to security are simply no longer sufficient. A CSO Market Pulse survey found that “two-thirds of security budgets are used to protect the network, with less than a third used to directly protect the data and intellectual property that reside inside the organization.”
• Insufficient governance. Forty-four percent of organizations have no formal data governance policy, and 22 percent of these companies have no plans to implement one, according to the 2013 Rand Secure Archive Data Governance Survey. Big data increases companies’ data ingestion by many orders of magnitude, adding to the complexity. Without overall management of the availability, usability, integrity, and security of big data employed in an enterprise, organizations will find it tough to address the mandates called for the U.S. Federal Trade Commission and the European Union.
Securing the big data life cycle requires that organizations address four overarching areas, according to the CSA’s Big Data Working Group:
• Infrastructure security. Secure computations in distributed programming frameworks as well as in nonrelational data stores.
• Data privacy. Secure the data itself using a privacy-preserving approach for data mining and analytics. Also, protect sensitive data through the use of cryptographically enforced data-centric security and granular access control.
• Data management. Manage enormous volumes of data using scalable, distributed solutions to secure data stores and enable efficient audits and data provenance.
• Integrity and reactive security. Use endpoint validation and filtering to check the integrity of streaming data, and real-time security monitoring and analytics to help prevent and address security problems.
Bottom line: “Organizations today require not only the right manpower, but a comprehensive set of policies, procedures, and technologies to responsibility guard sensitive information,” Mendelson says. “Unlike in the past, all these resources need to be continually tested, reviewed, and updated.”