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The Critical Security Issues in Big Data


The security issues in big data is a serious threat to the whole system. So, what security challenges that big data has in stock? Read on to know more…

Currently, Big Data is getting bigger and faster along the way than ever before and most organizations are scrambling to keep up with the pace. What makes data big, more defined, is that we have far more opportunities to collect it and from far more sources. For instance, think of all the billions of devices that are now Internet-capable like smartphones and Internet of Things (IoT) sensors. Now think of all the Big Data security issues that could generate!

Big Data Security Issues
While there are Big Data security and privacy concerns come with their own unique set of challenges, it is an extra reason to become aware of what such challenges are. It may come as a surprise that almost all Big Data security problems are due to the fact that it is big. It’s huge, actually.

Some of the security challenges that big data has in stock for organizations are:

Cryptographic Protection Issues: Encryption is a good way to protect sensitive data. Even though it is possible and recommended to encrypt all information, security protocols are often overlooked. Classified information is often stored on the cloud with no encryption whatsoever because it takes a lot of time to constantly encrypt and decrypt large amounts of data, thus taking away the biggest advantage of Big Data which is the speed.

Data Mining Challenges: These are the heart of many big data environments in several enterprises. For that very reason, it’s particularly important to ensure they’re secured against not just external threats, but insiders who abuse network privileges to obtain sensitive information — adding yet another layer of big data security issues. Here, data can be better protected by adding extra perimeters. Also, your system’s security could benefit from anonymization. If somebody gets personal data of your users with absent names, addresses and telephones, they can do practically no harm.

Access Controls: Just as with enterprise IT as a whole, it’s critically important to provide a system in which encrypted authentication/validation verifies that users are who they say they are, and determine who can see what. Sometimes, data items fall under restrictions and practically no users can see the secret info in them, like, personal information in medical records such as name, email, blood sugar, etc. But some parts of such items — free of ‘harsh’ restrictions — could theoretically be helpful for users with no access to the secret parts, say, for medical researchers. Nevertheless, all the useful contents are hidden from them.

Privacy Concerns: As more data is aggregated, privacy concerns will strengthen in parallel, and government regulations will be created as a result. On a broader context, the question “What is happening to my data, and where does it go?” will be asked not just in businesses and in government, but by intelligent and smart data security experts. Despite this, it’s hard to find security specialists who focus on big data security issues per se — largely because, historically, smart analytics and security haven’t always been ideal companions.

There is, however, an exception. Just as smart analytics tools can drive new business strategies, they can also drive security — given enough of the right information from the infrastructure, and the right algorithms to process it. And that, in essence is the basis of the emerging field of security intelligence, which correlates security info across disparate domains to reach conclusions. The solutions available, already smart, are rapidly going to get smarter in the years to come.


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