Big Data is a term that is used describe a large volume of data that floods a business every day. It can be used for analysis purposes, which can then be used to make important business decisions and important business moves. It can also be used to determine the causes of failures, generate coupons at checkout, recalculate risk portfolios, and find fraudulent activity before it ever has a chance to affect your organization. It is being used by all kinds of different organizations, including banking, government, education, healthcare, retail, and more. Big Data has become a hot topic. From a security standpoint, there are two important issues – securing your organization and customer information and using the data analyze and predict security issues. Here are 4 points to tackle Big Data security risks effectively.
Big Data can be, and is often, used for marketing and research. However, because it is so new, the fundamentals of using this information may not be done right, particularly from a security viewpoint. And, because it is a new technology, security gets tossed on the back burner. But when breaches are made in Big Data, such as through cyber attacks, there is a much greater risk for a ruined reputation and costly legal repercussions. These issues could potentially destroy your organization.
More and more companies are using the technology to both store and analyze all kinds of information, including clickstreams and social media content in order to gain a better understanding of their clients. This brings up the issue of ownership. The information needs to be classified, but it has proven to be quite the challenge. The owners of raw data, as well as the owners of Big Data process outputs, need to be identified.
Where the information is being stored and accessed is also important. It is highly unlikely that organizations will create in-house Big Data environments. Instead, the information will most likely be kept on the cloud. Just because it is there, though, this does not remove the responsibility of the organization to protect it. There are security measures, including attribute-based encryption, as well as antivirus software, that can provide some protection. What adds to the challenge is that many of these concepts are unfamiliar to many organizations.
Using Big Data for fraud detection has become an increasingly attractive option for many organizations. The overheads of managing output from the more traditional Security Incident and Event Management (SIEM) systems are beginning to prove too much for many IT departments. Big Data is viewed as a possible way to help. The challenge of both detecting and preventing treats could be addressed with Big Data analysis. It aims to detect threats in their earliest stages.
The logs that are used today are often ignored unless something goes wrong. With Big Data, however, there is an opportunity to consolidate and analyze logs automatically. In doing so, new insights are found that individual logs might miss. In turn, this an aid in enhancing intrusion detection systems (IDS), like Snort, as well as intrusion preventions systems (IPS). Snort is an open source intrusion detection system it is capable of real-time traffic analysis. It can do such things as protocol analysis, content searching, and detection of a variety of attacks. It can be used as a packet sniffer, a packet logger, or even a full network intrusion prevention system. Its main goal is to provide effective real-time defense against threats. Big Data can be used, at least, to improve better implementations of SIEM, IDS, and IPS.
One major technology associated with Big Data is Hadoop. While traditional data warehouses process structured data and can store large amounts of it, there is still a requirement for structure. This means that there is a restriction on what types of data can be processed. With Hadoop, however, large amounts of data can be processed regardless of whether it is structured or not. And, with the Hadoop Distributed File System, individual servers that are working in a cluster to fail without aborting the entire computation process. There are no restrictions on the data that this system can store.
Big Data seems very enticing to many organizations. It may offer several advantages, but there are still risks associated with it. These risks include:
a) The technology is still very new. Much like any other new technology, this means that organizations still do not understand it very well just yet. Because of this, it can introduce unexpected vulnerabilities, making it difficult to determine network security tips.
b) Open source codes are typically included with Big Data, which can result in unrecognized backdoors. It can also lead to default credentials. Both of these issues can leave you at risk.
c) It is possible that the attack surface of the nodes may not have been reviewed. The servers may not have been hardened sufficiently either.
d) The authentication of users and data access from several locations may not be controlled.
e) Log access and audit trails may be an issue.
f) There are plenty of opportunities for malicious activity, such as malicious data input and poor validation.
Big Data isn’t so much about the amount of data so much as it is about the techniques used for processing. In order to use Big Data effectively, specific specialist skills become necessary. As of right now, there is a shortage of individuals who specialize in the skills necessary for Big Data analysis. Hadoop and technologies that are similar to it are beginning to create the demand for individuals with specific skills. These skills include data mining, predictive modeling, content analysis, social media analysis, and more. Individuals who can implement Hadoop cluster, as well as secure, manage, and optimize them are also needed.
Big Data has expanded the boundaries of information and has opened up a whole new realm of possibilities. It has also introduced new risks and challenges for those who want to use it. It is possible, however, to tackle these risks and protect the information that your organization deals with.