Ensure Big Security With Big Data

The world is immersed in the sea of huge, recurring, and continuous data, and as the worldwide Big Data and Business Analytics market gains the biggest chunk, there are reasons to believe that organizations should look deeper into the big data security threats that will be raised because of the nature of these data sets.

Companies are collecting data related to consumer purchase behavior and bank transactions, and most of these data are continuously being transferred by the Internet of Things (IoTs). By nature, these data sets are very sensitive because they contain various personal details of consumers.

Big Data Security Practices

For big data, there are various data security practices, that are followed by companies; these are a few mentions:

1. Incident Alerting
2. Threat Filtering
3. Access Control
4. Activity Monitoring

Even with so many security protocols in place, it is sometimes really tricky to apply data security for big data environments because of the unique nature of the challenges.

Challenge One: The Data

As discussed above, the most challenging part of data security is the data itself, and the three v’s of big data play a crucial role here:

1. Volume: The ginormous volume of data that prevails in the data environment needs data security solutions to handle the threat against it, which means having an extremely scalable solution in place.
2. Velocity: In the world of Twitter, Facebook and other such social and enterprise apps, the limitless yet quick and real-time transfer of data means that you need a solution in place that can focus on data parsing and collection without crashing. Also, you need a scalable solution that can provide automation, delivering real-time visibility of security compromise and any such events.
3. Variety: Your scalable data security solution must provide robust audit capabilities due to the diversity of the data.

Challenge Two: The Data Environment

The associated infrastructure and technology present in the data environment, coupled with the multiplicity, make it a challenge.

1. Dispersed Data Stores: Big Data deployments have a multitude of geographically distributed data stores. This means multiple nodes require protection. This issue, in turn, increases the inconsistency in the security practices. To counter this issue, you need a solution that can provide strong, centralized administration capabilities.
2. Multiple Versions: Organizations develop their core building blocks using versions from multiple vendors, such as NoSQL and Hadoop. This creates huge complexity and diversity in the system, which needs to be solved with better security tools.
3. Multiple Layer: The Hadoop framework offers different layers of stack that offer distributed storage, table and schema management, and distributed programming tools. This creates multiple security threats on each point of life cycle.
4. Multiple Tech: Many organizations use multiple technologies for data storage and retrieval, making it tough for the data environment to provide ample security.

Challenge Three: The People

Sometimes the people working closely with Big Data fail to understand the degree of threat that is related to Big Data. We need to bifurcate this further to give a better insight:

1. Data Scientists: While working with structured and unstructured data, even with their vast experience and skill in handling such a variety of data, data scientists somehow do not give much importance to data security.
2. Developers: With its top-level access and the nature of its development stages, big data application development falls outside the security grid and is often the soft target of security breaches, sometimes via accident and sometimes due to hackers' malicious intent.

Big Data Environment Needs An Apt Security Channel

Many organizations of any size or shape cannot afford to lose time in researching, building, or developing a big data security solution. We are laying down some guidelines that might come in handy while deciding the type of security any organization will need:

1. A big data security solution that monitors and audits the access to sensitive data, continuously.
2. Uncovers any incident of unauthorized access and fraudulent activity in real-time.
3. Automates reporting and compliance activities.
4. Accelerates the incident response and forensic investigations.
5. Via trusted third-party integration, advanced anti-malware solutions are used to counter any form of cyber-attack or targeted attacks on the data environment.

Conclusion

Big data is going to be big in the coming years, and the volume of business that will be generated means that there will be constant threats lurking over the Big Data Environment. If you are looking for a big data solution that will aid you in overcoming the scalability, speed, diversity, and complexity of securing the big data environment, then you need a digital partner who understands your needs to the core.