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The Importance of Protecting Big Data and the Best Ways to Do It

The growth of big data over the past few years has been amazing to behold. Large corporations were the first to grab hold of the potential that big data entails, but as technology has progressed and become more available, smaller organizations are starting to adopt many of the same beneficial practices. The fact that big data has grown by leaps and bounds can be seen in the latest statistics, with one survey showing 48% of organizations expecting big data to play an important role within their business in the next three years. Big data growth rates have hit as much as 58% in 2013, and that rate is only expected to increase as big data becomes ingrained in regular business operations. But as with any widely used new technology, the use of big data has come with increased concern over securing that data. In a world rapidly transforming into one revolving around big data, the importance of protecting an organization’s big data cannot be overstated.

For many enterprises, big data can be seen as the lifeblood or fuel of the organization. What started out as an additional tool has quickly become the main ingredient for making business decisions. Big data can help increase an organization’s overall production, streamline the hiring process, help connect with customers, and even detect fraud. These are only a few applications that have been developed since big data became widespread, and more innovative solutions are sure to come as more varied industries put their own spin on big data analytics. From this standpoint, it’s easy to see why big data is so valuable to a business, even if placing a monetary value on it is difficult. That’s why protecting big data should be top priority, especially considering the long history of security threats that have affected systems and networks over the years.

Indeed, a failure to secure big data can be disastrous no matter which organization is the victim. According to a 2013 study by Symantec and the Ponemon Institute, the average cost of a security breach is more than $5 million. That number doesn’t even take into consideration the data breach at Target that happened at a later date, costing the retail giant more than $1 billion. Other more recent breaches, like that suffered by Home Depot, have also been quite costly. The damage caused by a security breach of even a small nature goes far beyond the financial toll as well. If private customer information is stolen and leaked, an enterprise may be subject to certain legal ramifications, such as lawsuits, and even fines depending on what laws are on the books. There’s also the reputational damage that can have a big impact on the organization’s bottom line. If a business loses customers due to a data breach, or if the general public no longer trusts them with personal information, the overall loss could be disastrous to the company. Needless to say, if businesses weren’t placing big data and network security as a top priority before, the latest headlines should motivate them to change their mind quickly.

Of course, securing big data is much easier said than done. A number of challenges have accompanied the flood of data now reaching businesses and organizations. For one thing, the data being fed into a big data system can present a formidable challenge. The volume and velocity of data being collected is immense, and while businesses have gotten better at collecting and analyzing this information, that still leaves certain security vulnerabilities in big data environments. The infrastructure, usually made up of multiple servers, can also be a complicated structure that introduces certain security weaknesses, making it difficult to implement the correct security controls.

In addition to these challenges, a special note should be made about the risks associated with big data technologies. Many big data programming tools were first developed without security precautions. A newly developed technology may lead to at least some security concerns at the outset, and the same has held true for these big data programs, which often feature vulnerabilities. The use of big data often leads to including open source code, which itself may unknowingly feature weaknesses like unrecognized backdoors and credentials, making it easy for attackers to exploit. Big data technologies may also be subject to insufficient authentication and access controls, increasing the chances of harmful data being introduced into the system. And since these are all new technologies and expanding fields, regulations are still in the early stages and likely don’t address many of the latest security threats.

When it comes to something as complex as protecting big data, it’s important to know that it’s not just about securing big data analytics; big data archives are also a crucial component that most businesses have in some form. Big data archives are usually comprised of old data that doesn’t need to be accessed as often for analytics purposes. While not used often, it’s still plays a vital role in the event old data needs to be examined. The best way to secure big data archives, protecting them from corruption and outside manipulation, is to provide frequent backups to the least used material. Placing big data on tape may sound like an outdated method, but it’s actually quite useful and keeps it free from security breaches and other forms of tampering. This also creates a separation between old data and the data most likely to be active for analytics. Further security measures can be employed for the active big data, while companies can rest assured the archived data is protected.

But that’s just for big data archives; big data that’s meant to be analyzed is a different matter. There are a number of best practices industry experts recommend when it comes to securing big data. The first is to treat data as a valuable commodity. While it may seem like a strange strategy, too often organizations will look at data as expendable, thus not giving data security the attention it rightfully deserves. Any big data project simply cannot function if there is no data or if the data is inaccurate, so doing everything it takes to make sure nothing harmful touches it is a worth the investment. This strategy isn’t necessarily one that requires adopting new equipment or techniques; it’s more of a change in mindset that will help all employees understand just how vital data is and caution them to ensure it is protected.

As for the technology used in big data analytics, companies may be tempted to use the fastest, most powerful processing tools available. Analytics engines are certainly an important aspect of any big data strategy, but the newest and fastest tools will likely come with security vulnerabilities. Any new tool needs to have the right procedures and policies in place before they are widely used within an organization. A little forethought and planning beforehand can actually save on a lot of headaches in the future since the more established technologies will have more effective safeguards at the ready.

Unknown ObjectAs mentioned earlier, certain big data tools can prove to be quite handy when dealing with large sets of data. Hadoop is one such tool, a platform designed specifically for data processing, but Hadoop was created without security in mind. Many updates have been made to the software that make up for this fact, but it’s also up to each organization to ensure their data is protected when using Hadoop and other platforms. Several tips are recommended, including using seamless encryption, which protects the data whether it is currently at rest or in motion. This encryption recommendation goes for big data no matter what it’s being used for or where it is located. Another big data platform tip is to ensure deployment and implementation in a cluster are done rapidly, which gives outside attacks less of a chance to interfere with the data.

For big data analytics, security controls should be a prominent feature. One of the most effective components of security controls includes ways for organizations to maintain and monitor audit logs. By monitoring big data clusters, data experts will be able to spot when something is suspicious and rectify the situation quickly. Companies should also make sure to use versions of open source software that have been secured with enhanced protections. Organizations also need the capabilities that allow them to monitor and control individual accounts. This gives them the power to control who has access to the valuable data while also letting them know when the data is being accessed and for what purpose.

Big data security is a rapidly developing field that is constantly trying to meet new and varied challenges. The consequences of failure can be catastrophic, which has led many organizations to adopt new technologies and procedures for handling data breaches. More has to be done to keep up with the changing threats that are out there, but as companies use big data platforms wisely and implement their own security controls, they’ll know their data is protected from those who would want to steal or damage it.