Big Data is all about voluminous datasets that can be analyzed to revive trends and patterns. The name “Big Data” refers to the size of data stored in massive size (such as: Petabyte, Exabyte, and Zettabyte). When it comes to Big Data, security becomes a prime concern for firms in order to protect their huge datasets.

By formulating better security and management, it will help firms in making their businesses even more efficient and effective; moreover, it’ll also assist in delivering data safely to targeted consumers. According to an industry source, 45% of firms believe that their prime concerns in Big Data management are:

Scarcity of data and

Cost of implementing data expertise.

Twenty-five percent of firms believe that, data accuracy and privacy are the two basic security issues while implementing Big Data projects. Users’ private data plays a vital role in any organization and it’s necessary to keep it protected from various security threats. One should try to balance between the utilization of the data and its privacy.

For knowing such Big data security threats faced by an organization, Big Data security analysis techniques help you to analyze such security issues. They are: cyber security, data privacy and security, data sharing and access of information, analytical challenges, and technical challenges, including heterogeneous data, fault tolerance, scalability and quality of data.

Technocrats have to re-think about their perception for an enterprise security by embracing security analysis. According to a source survey, security professionals have listed various tools for malware detection they are:-

Firewall logs-41 %

Server logs – 21%

ISD/IPS alerts – 27%

PC forensic data – 26%

IP packet capture – 22%

Role of Big Data in Security Analysis

Various threats are annihilating Big Data businesses. Another risk confronted by firms is: how to address such issues? And how to overcome such threats in order to ease the task of Big Data projects. For this, professionals need to address security issues for preventing such security threats that have attempted in manipulating your process.

For acknowledging big data security issues, you have to address threats from your process:





a. Users’ identity and authentication

It’s very important to verify whether users who have logged in are genuine or not. With security analysis, it makes users’ authentication task possible to monitor an enterprise security as a continuous process which includes identification of the users and their authenticity.

It helps you to keep track of the person’s suspicious behavior and users’ information over the website rather than just authenticating user’s access. (For example, when and where the user has logged in? Where was their last access over the web?)

b. Tracking Host Traffics

Are there any exceptions in the network traffics? Are there any encrypted data? Are there any skeptical data destinations? These kinds of exceptions can be uncovered using Big Data security analysis techniques by raising security questions.

c. Web Monitoring and tracking

You can also track security issues by observing suspicious activity that had been noticed in high-value application. With the use of SQL server logs, network session data, application logs and a transaction monitor; it is possible to trail over any suspicious data that had been transacted.

You should check what kind of data is exchanged or stored over the web. Also check whether the information that has been manipulated does not contain any valuable intellectual property.

d. Alteration in the infrastructure management

Firms must ensure that whether their security policies are matched with the complying standards or not. Also, check whether there is no alteration in the vulnerability management and configuration management to see if the server was manipulated or not.

After analyzing various security threats, let’s try to prevent and overcome them.

How to prevent the security breaches?

Security issues faced by most of Big Data businesses are due to the heterogeneous structure of datasets. Thus, it becomes necessary to structure your data in proper format by refining its quality standards. Apply the below prevention techniques for reducing your security breaches:

Affiliating risk and protection: Organizations, those that are implementing Big Data securities should make it a point to affiliate their risk and protection by combining log files and metadata.

Choose your data Sources: More data sources used, the more complex Big Data analysis will be. By this, there will be a greater chance of malware and other viruses affecting datasets of an organization.

Anonymous data traffic: One of the preventive security measures for detecting cyber security to support privacy regulation that is tending to anonymize your data sets. By removing the key components of data from the defined records, it helps you to protect sensitive information. For securing your datasets, you must implement the data encryption technique.

Data Governance: It is a relatively new concept for securing your voluminous datasets. This approach should be implemented by digital firms, employing big data, who wants to ensure the quality standards of datasets. If proper Data governance is not applied in an organization, it might miss-lead the data quality causing unexpected losses.

The high quality datasets you deliver to your audience, the more you succeed in your projects. For preventing such security threats one must utilize various tools and techniques to get rid of such security issues.

Anonymizing data, encryption tools, and real-time security monitor are big data security analyzing techniques. Moreover, it is also useful in tracking information over the web. Thus, it will help you to overcome and prevent various big data security threats by securing your datasets and website preventing from cyber crime.

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