Big Data Security: Security Issues And Challenges In The Queue

Large organization are leaping in for Big Data Security seeking the data protection. However, there are many challenges that need to be tackled in order to ensure privacy at all levels.


The era of big data security has brought up unparallel experiences with data points extending greater insights of better business decisions, drive exciting research and greater value to customers in many ways. In order to get through these outcomes, an organization needs to be quick and efficient enough, as this data is surely going to consist of some sensitive information.

However, there are many organizations which often hesitate to consider the security factor and even specifically encryption among the big data security solutions as they are generally considered about, deploying at scale or obstructing the analytics tools which make these solutions valuable at the first place. Well, we would, however, discuss all of it in a detailed manner.

What is Big Data Security?

Big data security
via: progress

Though there are very few readers who may be unaware of this term and concept. However, it is always good to start from the beginning. Big Data Security is the term used for collective addressing of the various measures and tools that are used to protect the analytics processes and data from thefts, attacks or any other malicious activities that can cause a negative effect for them.

Like any common cyber-security forms, big data variant considers attacks that had originated from any offline or online spheres. Big data security challenges for companies who operate on Cloud are known to be multi-faceted. The probable threats include theft of information stored online, DDoS attacks or ransomware that can even crash a server. When this information is sensitive or confidential, this issue can get even worse. Also, serious financial repercussions can be caused when big data storages of organizations are attacked. As a result, companies may have to face fines or sanctions, litigation costs or other losses.


How Can You Implement Big Data Security?

Data security
via: qubole

Large-scale organizations can use and implement various security measures in order to protect their big data analytics tools. Encryption is one of the most commonly used security tools this is a relatively simple tool, however, perfect to play in the long run. Encrypted data is of no use to hackers or any other external parties when they do not have the key to unlock it. Also, data encryption helps in protecting both input and the output.

Firewall is another efficient data security tool that can be used. It serves an effective purpose by filtering the traffic of the ones that are entering and leaving the servers. Organizations may thus prepare strong filters and avoid any risks from third parties and prevent any attacks before they happen.

Finally, controlling and considering the root access to BI tools and various analytics platforms may also help to protect your data. Opportunities for attacks can be reduced with a well developed, tired access system.

Big Data Security Issues

Big Data Security Issues
via: phys

It would be really difficult to describe Big Data in terms of size. These are datasets that cannot be processed in conventional databases with respect to their size. Data accumulation in this manners helps in the improvement of services in many ways. However, when there is such huge data to deal with, privacy issues are certainly expected at a point.  These issues though make-up Big Data Security to be a prime concern for any organization in that case. Acknowledgment of these threats and various measures to deal with them are therefore being brought up by organizations.

Why Are Big Data Security Issues Evolving?

Big Data is undeniably nothing new for large organizations; however among medium-sized and small organizations also it had been able to gain probable popularity because of the low-cost services and ease of data management offered by the platform. Data mining and collection is facilitated well with cloud-based storage. However, the cloud storage and big data integration have given rise to various privacy and security threats.

The reason behind such breaks may likewise be that security applications that are intended to store certain measures of information can’t the huge volumes of information that the previously mentioned datasets have. Another important fact is that these security technologies, being inefficient, can control only static data and lack the capabilities of managing dynamic data.

Therefore a mere regular security check may be inefficient for detecting security patches in case of continuous data streaming. Thus, while big data analysis and data streaming full-time privacy is a major consideration. Mentioned below are some points that may help well with these:

Protecting Data and Transaction Logs

Big Data Security Issues
via: compudata

Data that is stored in any storage medium like transaction logs and any other sensitive details are expected to have varying levels. However, that would never be enough. For instance, consider that IT manager is getting the insight of data transfer between the two levels. Data size, in that case, is known to increase continuously. The availability and scalability emphasize the need of auto-tiering for big data storage management. However, since the auto-tiering method cannot keep track of data storage location, there are yet new challenges to be popped-up with the same.

Security of Distributed Framework Calculations and Other Processes

Security protections are often known to be lacked by computational security and other considerable digital assets which are a part of a distributed framework such as the MapReduce function of Hadoop. Data security and securing the mapper, in the presence of an unauthorized mapper are the two major ways of dealing with this issue.

Filtration and Validation of End-Point Inputs

End-point devices are known to be the major factors of big data management. End-point devices support for processing, storage, and other necessary actions that are known to be performed with the help of input data. Therefore the use of authentic and legitimate end-point devices should always be considered by an organization.

Real-time Protection and Data Security

Organizations are unable to manage and maintain regular checks because of large amounts of data generation. However, observations or security checks in real time are the most beneficial actions that can be taken.

Protecting Access Control Method Communication and Encryption

Big Data Security Issues
via: xenonstack

Secured data storage would always be an intelligent step with respect to data protection. However, encrypting access control methods becomes necessary because of vulnerable data storage devices in most cases.

Granular Auditing

There can be several advantages of analyzing different logs these details may also prove to be useful in recognizing any cyber attacks or other malicious activities. Regular auditing is, therefore, considered to be really lucrative.

Data Provenance

For proper classification of data, it is very necessary to recognize its accurate origin, validation, and authentication, and access control can be gained.


Granular Access Control

Mandatory access control and a strong authentication process are needed for granular access control of big data stores by Hadoop Distributed file system or NoSQL.

Privacy Protection for Non-Rational Data Stores

There are many security vulnerabilities with data stores like NoSQL. These vulnerabilities probably cause privacy threats. A prominent security flaw recounts the inability to encrypt during logging of data or tagging or distributing it to other groups while it’s being collected or streamed.

Organizations should, therefore, take care that all the big databases are set perfectly set for a face-off with various vulnerabilities and security threats. Real-time protection and other considerable security protections should be fulfilled during data collection. The importance of extraordinary efforts should be considered with respect to Big data security and the huge size that needs to be dealt with.