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Top 10 Security Challenges For Big Data In B2B

Top 10 Security Challenges For Big Data In B2B

We cannot describe Big Data in terms of size. To understand the basic concept, we can only say that Big Data is a form of Datasets which cannot be processed in the way of manual databases as per their size. This type of data accumulations is very helpful for customer care centers to improve their services. However, these kinds of huge databases need high privacy. Business organizations which are using Big Data should be concerned about the security of the databases.

Top 10 Security Challenges For Big Data In B2B

Big data is a new technology in the world of large organizations. But these days small and medium scale organizations are also paying importance to the technology. To facilitate data mining and collection, cloud-storage plays an important role. If you combine the cloud storage and big data, then you face some big data security challenges as well. Below stated list are the common challenges which companies face in case of big data in b2b.

  1 Transaction logs and data protection

In big data, data storage is a medium to keep data safe. The technology no doubt uses so many levels to store the data, but I guess this is not enough. The IT manager’s insights should be available while transferring data between these levels. The size of data will increase continuously so as its scalability. Due to this reason, auto-tiering is important in storage management. Every day new challenges are being imposed on the big data storage capabilities and auto-tiering methods to keep record of the data storage locations.

2 End-Point inputs should be validated and filtered

The main factors of maintaining data are end-point devices. Input data is the only key to perform data collection, processing, storage and other necessary actions on processed data. The organizations should use authenticated and legitimated endpoint devices to make it possible.

3 Security of distributed framework calculations

Security protections are mostly lacked in distributed frameworks due to computational security and other digital assets. You need to secure the mappers and also protect the data from unauthorized mappers. This is very important in the case of a distributed framework because of the sharing of usage of same data storage space. And you may face conflicts due to which your data will push into someone else’s hands.

4 Security and protection of data in real-time

To secure data in real-time scenarios is the most difficult thing in all types of databases.  Due to the generation of a large amount of data, most of the organizations forget to keep track. They may be unable to maintain the regular checks on the data. But to maintain security and data accessibility, organizations have to check the security of the data in real-time as well.

5 Protecting communication and encryption

To choose a secured device for data storage is an intelligent move to keep data safe. It is necessary to encrypt the methods of access control due to the vulnerability of the storage devices. To encrypt the data and communication methods is important to keep it safe from hacker’s attacks and unauthorized interventions in the communication methods.

6 Data Provenance

  It is very important to know the nature and behavior of data to classify it. Along with that, the origin of data is also important for validation, authentication, accuracy and access controls. There is no doubt that big data take care of the data identification and classification but still, you have to be sure at your end as well.

7 Granular auditing

The organizations have to analyze different types of logs. This is beneficial because by analyzing logs you can easily prevent your data logs from cyber-attacks and malicious activities. You have to perform regular audits to make the trick successful.

8 Non-Rational data storage protection

Data stores which are not based on SQL are known as Non-Rational data stores. These kinds of data stores may have much vulnerability that causes privacy issues. It is difficult to encrypt data at the time of logging, tagging and distributing in other groups. Because this is the time when the data collection process is going on and, in that phase, it is not easy to encrypt data.

9 Granular access controls

 In big data, distributed file system needs a strong authentication process. The reason behind this is that these kinds of databases are not secured by SQL queries and people who don’t know queries can easily access it. Now it is difficult to differentiate between an authorized and unauthorized user in this case. So, it is the responsibility of the organization that they have to keep the data safe.

10 Collaboration with other teams

Big data is a bit of a complex structure to use. And to use big data so many technologies are required. This is the reason why the higher officials have to share the data with the technical people. Now when data is going be shared with so many people nobody will know if someone misuses it. The organizations have to be sure while sharing data with other team members of the organization.

Conclusion

Organizations must understand that Big data is prone to security and privacy threats. While data performing the collection process, they have to take all precautions to take inputs correctly. Because real-time data transfer is the most critical step of big data. Also, when it comes to transaction logs and data logs, I would like to say that the organizations have to check the things manually as well instead of fully being dependent on the technology. Major data security breaches happen only due to the carefree behavior of the responsible persons.

The above-stated issues may vary according to different organizations and quantity of their data storage. Always keep in mind that the big size of data is difficult to store and maintain. But by taking all the precautions you can make it possible.

If you have queries regarding big data, then please ask in the comments section. For more interesting information for big data keep reading and stay connected with us.

Author Bio : Manikandaprabhu Sivagnanam

Manikandaprabhu Sivagnanam is a sportsman, reader and  big data enthusiast. He is passionate about digital marketing and he explores new technologies.

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