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3 Big Data Security Challenges on the Horizon

May 05, 2017

3 Big Data Security Challenges on the Horizon

The Big Data market continues to grow, with Wikibon forecasting that it will reach $28.5B in 2014 and $50B in 2017. But with increasing adoption comes a corresponding increase in Big Data security challenges. As we know, challenges can provide opportunities for VARs prepared to expand their knowledge base and business. Are you up to the task? Here are three Big Data security challenges to keep an eye on in the coming months and years.

1. Data privacy in distributed environments

Big Data poses a number of security challenges not just because of the type of data collected—often, Personally Identifying Information (PII) protected by various industry and government regulations—but also because of the amount of data collected. As It Business Edge's "Big Data Security Risk in the Enterprise: The Pitfalls of Hadoop" slideshow points out, "Traditional security technologies have been built on the concept of protecting a single physical entity (like a database or server), not the uniquely distributed Big Data computing environments characterized by Hadoop clusters." Hadoop itself was originally built to process public data, not private. Organizations in security-conscious verticals, like healthcare, financial services, and government, will most likely need to implement additional, Big Data-specific security measures not found in their baseline Big Data infrastructure.

2. Secure data storage in Big Data storage architectures

The storage of all that data creates yet more Big Data security challenges. It must be stored in a way that allows rapid retrieval of often large data sets and creates demands that traditional storage architecture often can't handle. To address this, many organizations implement auto-tiered storage solutions. In these solutions, policy-based automation handles the transfer and storage of data sets among various storage "tiers" that utilize media ranging from slower but less expensive to faster and more sophisticated. Automation also reduces direct control over the transfer and storage of that data, however, and moving sensitive data to the lower, less secure tiers of storage can open enterprises up to disastrous breaches. VARs up to the task of developing auto-tiered storage policy scripts and algorithms that prevent this from happening stand to benefit.

3. Big Data and regulatory compliance

Consumer data—in particular, identification, health, and financial data—is protected by a complex web of industry and government data privacy regulations. Organizations that handle sensitive data must remain in compliance with those regulations or face heavy fines and, in some cases, prosecution. Regulatory compliance is therefore a big deal to the enterprise, and Big Data security challenges can complicate matters. Organizations must find ways to encrypt sensitive data, develop and enforce strict access controls, and generate clear and thorough logs and audit trails in order to stay compliant. VARs well versed in Big Data-appropriate security solutions that fit these needs and play well with distributed storage and processing architectures can leverage that knowledge to address customers' compliance concerns.

As you can see, Big Data security challenges make implementing a Big Data solution more complicated than it might seem. The challenges aren't insurmountable for the knowledgeable VAR, though. If you're ready to explore Big Data and what it can do for your business, check out some white papers and have a conversation with one of our data center specialists to find out what skills you need and how to learn them.

What Big Data security challenges are you seeing in the marketplace? Let us know in the comments.