Big Data security

Big Data is quite popular. Nowadays it is not only for IT companies. Big Data is widespread and actively used in various fields. The quantity of data worldwide is growing at great speed and it’s harder and harder to process it efficiently. Thus said, Big Data helps manage a huge amount of information and make necessary conclusions or predictions. Big Data is closely related to Machine Learning (ML), Artificial Intelligence (AI) and Data Science.

The neural networks for complex systems can be trained because of Big Data and ML. A lot of systems you work with every day are possible thanks for Big Data. So, nobody denies Big Data benefits. You can get acquainted with Big Data features more closely here

Today we will talk about some challenges related to Big Data. Especially about security – the main of them.

When implementing Big Data analytics, the business founder must be thinking about data security.

Big Data security issues

The business collects a lot of information, including sensitive data, and the security issues are quite real. Usually, information security is provided by a complex approach.

  1. Protecting data transactions and logs. When the data is stored on the physical medium, it includes information like logs, transactions, etc. Information about data might be structured at various levels — it is called the auto-tiering storage method. Such data distribution provides security.
  2. End-point inputs validation. End-point inputs maintain the storage where data is processed. It’s very important to check the authenticity of end-point devices and protect them.
  3. Real-time protection. It is quite difficult to monitor a big amount of data, but the most efficient way to protect data is by checking data in real time.
  4. Granular auditing and access control. The strong access and authentication control and granular auditing highly increasing the security of Big Data.
  5. Data origin and classification. The origin of data is important. You can collect data from different sources, some of them will be reliable and some can contain viruses, for example. As a result, all the data will be damaged. So, you need to track data movements.
  6. Encryption. This is a basic way to protect your data but a lot of companies ignore this moment and store sensitive data in the cloud without encryption. Sensitive data encryption is usually overlooked because it slows down the data processing a little. This is a potentially dangerous situation. You should always encrypt your sensitive data.
  7. Security audits. Monitoring of Big Data security is a very important part of data protection. In reality, the lack of recourses and qualified specialists makes audit impossible.

Conclusion: is Big Data security so scary and complicated?

We have talked about Big Data cybersecurity, let’s wrap it up. As you can see, the security of Big Data analytics really looks quite complicated and hard to solve. Do not hurry to ignore Big Data analytics implementation in your company. With the right approach, your data will be safe and your business will prosper. All you need is an efficient implementation plan that includes the security strategy.

To solve it you can refer to the Managed Service Provider (MSP) who works with Big Data. Experienced MSP like IT Svit can implement Big Data analytics and security without any data loss or damage. Take into account MSP reputation because unscrupulous MSP can harm your company processes. Thus said, try to find feedback about an MSP, check ratings and ask questions directly to MSP. Good specialists will gladly answer to you and implement Big Data security at its best.

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