The Basics Of Big Data Security

The Basics Of Big Data Security

By: rkspark

Big data is a lot of data and therefore a lot of responsibility. So, how do you keep it safe – here are some tips.

Discovering And Understanding Sensitive Data

If you were to ask 5 of your colleagues about what data records makeup payment card information, it is likely that you will get 5 different answers. This is why you need to have a cross-functional team which decides what sensitive data is and what needs to be protected before you roll out any data protection strategies. It is important to note that not all data is high risk.

There are many businesses which have failed because they do not understand the distributed data landscape and where their sensitive data is located. When looking for this location, you need to remember that sensitive data is often duplicated and shared across your production systems. The data can also be shared with non-production systems and third-party systems run by vendors and business partners.

Monitoring And Auditing Data Activity Without Slowing Performance

If you are looking for complete insight into the who, what, when and how of your data transactions, you need to complete monitoring and auditing of data activity. When you use complete access history, you will be able to better understand the data and the application access patterns. This will also help you prevent any data leaks, respond correctly to suspicious activity in real time and enforce data change controls.

You are also able to get automated compliance reports on a schedule with the use of leading monitoring solutions. These solutions also allow you to distribute the reports to oversight teams so they can electronically sign them off. You can also escalate and document any results from the remediation activities that you have undertaken. Of course, you need to be careful with solutions that rely on native logging as they could slow down instead of supporting your real-time analysis. This piece from Capita ITPS showcases how banks need to deal with big data and is a good general blueprint for lots of other industries.

Masking Of Sensitive Information

You need to mask sensitive information in databases, applications, analytics, reports and documents to ensure that information analysis and sharing does not breach data privacy rules. Many people do not realize that it is possible to mask this information in applications without breaking anything. The technology known as semantic masking de-identifies the data based on rules you have set. The primary value of semantic masking is that you can retain the usefulness of the information without risking the sensitive data.

Every day, there are 2.5 quintillion bytes of data being created which makes now the best time to understand sensitive data and establish your business security policies. These policies need to keep the customer, business, and personal information along with other sensitive data safe. Focusing on the discovery, monitoring, auditing and data masking of the information will be the best foundation for your policy.

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