Big Data could not be described just in terms of its size but there are many other factors. However, Big Data are datasets that can’t process in conventional database ways to their size. This kind of data collection helps improve customer care service in many ways. Moreover, such huge amounts of data can also bring forth many privacy issues. This makes Big Data Security a prime concern for any organization.
Working in the field of data security and privacy, many organizations are acknowledging these threats and taking measures to prevent them. Here are Big Data Security and Privacy Challenges that organizations face.
WHY BIG DATA SECURITY ISSUES ARE SURFACING
Big data is nothing new to large organizations, however, it’s also growing popular among smaller and medium-sized firms due to cost reduction and provide ease to manage data.
Cloud-based storage has facilitated data mining and collection. However, this big data and cloud storage integration has caused a challenge to privacy and security threats.
The reason for such breaches may also be that security applications to store certain amounts of data cannot the big volumes of data that the aforementioned datasets have. Also, these security technologies are inefficient to manage dynamic data and can control static data only. Therefore, just a regular security check can not detect security patches for continuous streaming data. For this purpose, you need full-time privacy while data streaming and big data analysis.
PROTECTING TRANSACTION LOGS AND DATA
Data stored in a storage medium, such as transaction logs and other sensitive information, may have varying levels, but that’s not enough. For instance, the transfer of data between these levels gives the IT manager insight into the data. Data size is continuously increasing, the scalability and availability make auto-tiering necessary for big data storage management. Yet, new challenges are being posed to big data storage as the auto-tiering method doesn’t keep track of data storage location.
VALIDATION AND FILTRATION OF END-POINT INPUTS
End-point devices are the main factors for maintaining big data. Storage, processing, and other necessary tasks performed with the help of input data, which is provided by end-points. Therefore, an organization should make sure to use an authentic and legitimate end-point device.
SECURING DISTRIBUTED FRAMEWORK CALCULATIONS AND OTHER PROCESSES
Computational security and other digital assets in a distributed framework like the MapReduce function of Hadoop, mostly lack security protections. The two main preventions for it are securing the mappers and protecting the data in the presence of an unauthorized mapper.
SECURING AND PROTECTING DATA IN REAL-TIME
Due to large amounts of data generation, most organizations are unable to maintain regular checks. However, it is most beneficial to perform security checks and observations in real-time or almost in real-time.
PROTECTING ACCESS CONTROL METHOD COMMUNICATION AND ENCRYPTION
A secured data storage device is an intelligent step to protect the data. Yet, because most often data storage devices are vulnerable, it is necessary to encrypt the access control methods as well.
To classify data, it is necessary to be aware of its origin To determine the data origin accurately, authentication, validation, and access control could be gained.
Analyzing different kinds of logs could be advantageous and this information could help recognize any kind of cyber-attack or malicious activity. Therefore, regular auditing can be beneficial.
GRANULAR ACCESS CONTROL
Granular access control of big data stores by NoSQL databases or the Hadoop Distributed File System requires a strong authentication process and mandatory access control.
PRIVACY PROTECTION FOR NON-RATIONAL DATA STORES
Datastores such as NoSQL have many security vulnerabilities, which cause privacy threats. A prominent security flaw is that it is unable to encrypt data during the tagging or logging of data or while distributing it into different groups when it is streamed or collected.
Organizations must ensure that all big databases are immune to security threats and vulnerabilities. During data collection, all the necessary security protections such as real-time management should be fulfilled. Keeping in mind the huge size of big data, organizations should remember the fact that managing such data could be difficult and requires extraordinary efforts. However, taking all these steps would help maintain consumer privacy.