Big data is one of the top trending technologies today. I think it’s safe to assume that where there’s big data, there’s valuable information. And where there’s valuable information, there are security threats.
Obviously, we need to secure our data, and that’s what I’ll be talking about today. I’ll tell you what big data security is and talk about the challenges you’ll face with it.
The major topics I cover in this post are:
- What is big data security?
- Challenges of big data security
- Technologies used for big data security
By the end of this post, you should have an understanding of what big data security is, why big data security is important, where you would face challenges while setting up security for big data systems, and the approaches you can utilize to provide big data security.
What Is Big Data Security?
Big data security is similar to data security, but the main difference is that big data security is more specific to big data architecture and systems. In a nutshell, big data security is a methodology to secure big data using various techniques and tools, which we’ll discuss later in this post.
Now, you might be wondering, “What or who do we need to secure big data from?” You need to secure it from attacks, theft, misuse, or anything that would compromise its integrity.
The most common threats to big data systems are information theft, DDoS attacks, ransomware, and erasing data. Just to give you a brief idea, let me explain each these:
- Information theft: This threat is self-explanatory. It’s basically when a hacker steals sensitive information from a big data system.
- DDoS: This stands for distributed denial of service, which is when a hacker does something to the big data system to make it deny service to the users that it should be providing services to. You can learn more about DDoS attacks here.
- Ransomware: This is one of the most deadly threats where the hacker encrypts all of your data and, in order to get your original data back, you need to pay a ransom to the hackers.
- Erasing Data: This again is self-explanatory. This is when a hacker erases all the data on a big data system.
Taking measures to prevent such threats and securing your data is big data security. These measures include setting up firewalls, strong user authentication, IPS (intrusion prevention systems) and IDS (intrusion detection systems).
When it comes to big data security, there are three stages to be concerned about:
- Incoming data
- Stored data
- Outgoing data
Let’s discuss these next.
A big data system gets its data from various sources. These sources include CRM (customer relationship management), ERP (enterprise relationship management), emails, etc. The first of your concerns is to ensure that incoming data is not malicious.
A hacker might include malicious code in the incoming data, which would start doing its job once it’s in your system. So you have to make sure that the incoming data goes through a security check. One of the ways to do this is by using firewalls to prevent malicious data from getting into your system.
Once the data is in your system, you should make sure its integrity is not compromised. To do this, you will have to use encryption techniques to maintain data confidentiality and strong authorization to avoid unauthorized access to data.
To implement this throughout the system, you will have to make use of security tool sets such as TrueCrypt, OpenVAS, ZAP, etc. These tool sets will help you implement the above-mentioned approaches as well as monitor whether your security is working as expected throughout the system.
You might be thinking, “I control outgoing data. Why should I think about security when I decide what data is to be sent outside the system?” Ever heard of information leakage? This is when your system gives out information that it is not supposed to give.
A major function of big data systems is to process data in a smart way to make it available to the end user efficiently. A lot of thought goes into designing an efficient system. But creating an efficient system also adds a lot of flaws in the system if not properly taken care of.
Hackers can find sensitive information or discover access points through these flaws.
Information leakage is just one of the issues associated with outgoing data. There have also been cases where the big data system has to send data to another system outside its domain and, due to lack of security, hackers intrude and steal sensitive information.
Thus, you should take care of security for outgoing data as well. One of the best ways to do it is by using encryption.
Now that you have a fair idea about big data security, let’s look into the challenges involved in big data security.
Challenges of Big Data Security
You should know that handling and processing big data is a challenge in itself. Securing your big data adds a separate challenge. I’ve made a list of a few challenges that you might face while implementing big data security:
- Handling security for unstructured data
- Distributed frameworks
- Providing real-time security
- Endpoint security
- Data provenance
Now let’s look at each of these in detail.
Handling Security for Unstructured Data
Big data systems collect a lot of unstructured data. The main sources of these data are NoSQL databases. These NoSQL databases lack security, so you should take care that there is no malicious input coming to your big data system from this source.
Big data systems usually use distributed frameworks; Hadoop is one of the popular ones. The data in these frameworks are stored and processed on different nodes to increase performance. This makes it challenging to implement security on all these nodes.
Providing Real-Time Security
In many cases, big data systems are used for real-time data processing, which means a lot of data is flowing through the system in a short time. Providing security to such systems without affecting the system’s performance becomes challenging.
A big data system gets its data from multiple sources and sends the data to various destinations. All these sources and destinations are endpoints and potential threat points. You must take great care to provide security against any potential threat from these endpoints, especially the source.
Data provenance means keeping a record of the details of the data, such as origin, changes made to the data, who accessed it, etc. It’s basically the metadata of the data.
Big data has a huge amount of data. Managing its metadata adds even more data to the big data system. So, one more challenge that adds up to your big data security implementation is providing security to the metadata.
Now that you have an idea about what big data security is and what challenges you will face while setting it up, let’s have a look at the most commonly used big data security technologies.
Technologies Used for Big Data Security
Here’s a list of some of the most common big data security technologies:
- Access control
- Intrusion detection systems (IDS)
- Physical security
Whenever you want security, encryption is your best friend. Encryption is being used almost everywhere today and is really useful for keeping data safe. There are a variety of tools and techniques that you can use, depending on your use case. For example, you can use an algorithm like AES (advanced encryption standard) or private and public key encryption.
Access control is intended to limit the user from accessing data that they are not supposed to access. The idea is that if you can’t reach the data, you can’t attack the data. Implementing access controls adds security against both external and internal threats.
Intrusion Detection Systems
Even if you think you’ve implemented all the possible ways to secure the big data system, there’s a chance that you’ve missed something. That’s why you should use intrusion detection systems (IDS). An IDS lets you know when there’s an attack so you can take action on it.
Last but not least is to provide physical security. The physical hardware of a big data system has to be well secured so that no unauthorized person can access it.
By now, I think you have realized how important it is to have security for a big data system. As mentioned in this post, there are challenges in providing security but there are also different ways (as listed above) that you can make use of to secure your big data security.
This blog post will give you an idea of how to mitigate threats.
Well, folks, that’s all for now on big data security. I hope this post was informative.
This post was written by Omkar Hiremath. Omkar uses his BA in computer science to share theoretical and demo-based learning on various areas of technology, like ethical hacking, Python, blockchain, and Hadoop.