Everything You Need to Know About Big Data Security Analytics

Are you thinking about becoming a business analyst or improving your analytical skills? What skills are required for a job in business analytics? Is it a single skill or a collection of skills? How does it operate in practice? The answers to these – and many other – questions are sown into this article.

Big data security analytics collects massive security datasets that are difficult to analyze using conventional security data processing software or on-hand database management tools. The latest big data techniques are used to secure them.

Big Data Security Analytics
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It is not uncommon to hear a plethora of new words coined every day as technology is being upgraded and invented at a similarly fast pace. The words ‘big data’ and ‘data security’ are thrown around often when topics related to maintaining and sustaining the information of major companies and businesses are talked about. Similarly, the word “analytics” is part of the discussion as well. More and more companies are looking for people with data analytics knowledge and experience to assist them in sorting through their large amounts of data, also known as big data. 

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The Purpose of Big Data Security Analytics

While security analytics have been around for a long time, the introduction of big data in this field has changed things a lot. Not only can the security analysts leverage the advantages provided by big data, but they can also employ advanced analytics techniques using Machine Learning.

Conventionally, cyber analysts or security analysts were limited to techniques like analyzing network vulnerabilities or correlation analysis; big data lets them use ML techniques like anomaly detection to identify minor and hidden irregularities in networks. As you can imagine, it was close to impossible to put such techniques in place if it wasn’t for big data.

Big data is all about detecting patterns and using them to understand the behavior of something based on past data. And just like every other field where big data is used, cyber security also has a lot to benefit from—for example, identifying patterns in network traffic and automatically spotting anything out of the usual, catching potential threat.

Hence, security big data analytics combines the powers of cyber security with big data techniques to ensure the safety of confidential network data and eliminate the chances of data breaches.

Five Types of Big Data Analytics

Now that you know what big data analytics refers to let’s move on to the five primary types one can master and make the most of.

1.     Prescriptive Analytics:

Prescriptive analytics is one of the three primary forms of analytics businesses use to examine data. Prescriptive analytics develops the best potential recommendations for a scenario as it develops, based on what the analyst can deduct from the available data. Prescriptive analytics concerns the present; whereas predictive analytics is related to the future, and descriptive analytics is concerned with the past.

Where Can We See It Being Used?

Prescriptive analytics in action can be seen in Google’s self-driving automobile, Waymo. Every trip, the vehicle does millions of computations to determine when and where to turn, whether to slow down or speed up and change lanes – all decisions that a human driver would make behind the wheel.

2.     Predictive Analysis:

Predictive analytics uses statisticsmodelingdata mining, and machine learning to weigh in on proposed data trends to foretell the future. It is the most frequently utilized and highly accessible kind of analytics. It focuses on forecasting the outcome of specific scenarios about various possible answers from a corporation for a particular circumstance. 

Predictive analytics models come in various shapes and sizes, but they always use a scoring system to determine how likely a result occurs. Decision analysis and optimization, transactional profiling, and predictive modeling are the three pillars of predictive analytics. Predictive analytics looks for trends in transactional and historical data to detect risks and opportunities.

Predictive analytics analyses current data and forecasts what might happen in specific circumstances; therefore, it may use data mining, artificial intelligence, and machine learning methods.

Where Can We See It Being Used?

  • In healthcare, predictive analytics ensures that patients who require urgent care can receive it faster by anticipating and determining high-risk patients.
  • In manufacturing, manufacturing managers can monitor the status and performance of equipment and predict breakdowns before they occur by incorporating predictive analytics into their applications. They can plan and reallocate the load to other machines to minimize production disruption.
  • For financial professionals, the predictive analytics system can investigate a company’s or individuals’ demographics, items they’ve bought/used, payment history, customer service records, and any recent harmful incidents.

3.     Diagnostic Analytics:

As the name implies, Diagnostic Analytics determines the cause of a problem. It provides a deep and in-depth understanding of a problem’s core cause. 

For the cause behind a given occurrence, data scientists resort to analytics. Diagnostic analytics techniques include drill-down, data mining, data recoverychurn reason analysis, and customer health score analysis

Diagnostic analytics is helpful for companies when looking at the causes of leading churn indicators and using trends among your most loyal clients.

Big Data Security Analytics
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Where Can We See It Being Used?

An e-commerce company can be an excellent example of diagnostic analytics in action. Given a situation that, despite customers adding things to their carts, its revenues have decreased.

The possible reasons behind this problem can be: 

  • The form did not load correctly.
  • The shipping prices are too high.
  • There are not enough payment methods accessible.

The organization uses diagnostic analytics to identify a precise explanation for the problem and address it.

Benefits:

Every business is increasingly reliant on data. By turning your complex data into visualizations and insights that everyone can understand, diagnostic tools will help you get the most out of it. Diagnostic analytics enables you to extract valuable data by posing the right questions and conducting in-depth investigations into the responses.

4.     Descriptive Analytics:

Descriptive analytics is the most basic and common type of analytics companies use. It is a good tool for identifying patterns within a specific client segment. It condenses and summarizes previous data into a digestible format. 

Descriptive analytics provide insights into what has happened and tendencies that can be investigated further. This aids in creating reports such as a company’s income, earnings, and sales, among other things.

Where Can We See It Being Used?

  • Reports: Descriptive analytics generates the primary financial metrics in a company’s financial statements. Descriptive analytics is also used in other typical reports to highlight aspects of business performance. 
  • Visualizations: Using charts and other graphic representations to display metrics might help you explain their significance to a larger audience. 
  • Dashboards: Executives, managers, and other staff can utilize dashboards to keep track of their progress and manage their daily tasks. Dashboards provide a selection of key performance indicators (KPIs) and other relevant data personalized to the individual’s needs. To help individuals digest the information more quickly, it may be portrayed as charts or other visualizations.

5.     Cyber Analytics

Cyber analytics is a new and rapidly growing skill set in the business and data analytics industry, combining cyber security expertise with analytical understanding. While the number of internet-connected devices continues to grow, cybersecurity attacks have increased in volume and sophistication. 

The demand for large data sifters with an IT background is being met by cyber analysts. Cyber analysts utilize complex and powerful tools and software to find weaknesses and cut off attack vectors using a data-driven approach.

Where Can We See It Being Used?

  • Analyzing network traffic to spot trends that could signal an attack. 
  • Detect harmful activity or insider threats. 
  • Forensics and incident response. 
  • Manage the risk of third- and fourth-party vendors. 
  • Determine whether data has been stolen and which accounts may have been hacked. 
  • Governance, risk, and compliance are all critical factors to consider. 
  • With threat hunting, you can identify threat signs.
Big Data Security Analytics
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Big Data Security Analytics Use Cases

As we previously talked about, big data security has a lot of use cases, and all of them are equally important. Let’s discuss some of the most widespread use cases of big data security.

1.     Network Traffic Analysis

When we talk about large-scale companies, it’s not unusual for the traffic moving in and out of the network to be pretty huge. Such traffic makes it tricky for network analysts to keep track of everything, and anomalies often go unnoticed.

However, with big data security analytics having an eye over the network, there are no outliers or exceptional cases unnoticed. Security analysts can easily keep track of everything related to the network traffic.

2.     User Behavior Analysis

The company’s customers are constantly interacting with the workflow of the IT infrastructure. Still, conventional ways of network monitoring don’t allow the security analysts to keep a tight check over every user’s behavior, mostly since very time-consuming when the customer pool is large.

But big data security analytics tools allow this and help the security analysts run customer-level analysis without consuming a lot of resources. Not only does this help build user profiles, but it also helps eliminate internal threats.

3.     Threat Hunting

Threat hunting has always been a hot area when it comes to cyber security. It has been in working long before big data came into being. However, ever since big data security analytics has boosted, threat hunting has become much more efficient.

Not only can we automate threat hunting using big data analytics, but we can also widely increase its scope, making it much more fruitful.

4.     Cloud Security Monitoring

Cloud computing is among the hottest topics in the IT industry right now, and companies are giving in to cloud infrastructure in flocks. Big data security analytics also happens to provide an excellent cloud monitoring system with a minimal need for manual participation.

Top 3 Big Data Security Analytics Tools

There are many tools used in the industry when it comes to big data security analytics. While some focus on specific scenarios, some are generally good for all use cases. Here we will discuss the top three generic ones you could look into.

1.     LogRhythm

LogRhythm is amongst the leading SIEM platforms that deliver comprehensive security analytics. Its services are used worldwide and include User and Entity Behavior Analytics (UEBA), Security Orchestration, Network Detection and Response, and so on.

It has served various big clients over the years, such as NASA, Gartner, Cargill, etc.

2.     RSA Security Analytics

RSA security is another popular security analytics service that provides security analysts with many pre-built reports that help them jump on to network analytics quickly. It uses the data collected by the network in a very efficient manner.

Moreover, it includes the RSA Live service to assist with data processing and correlation rules. So, if you want to work on a small scale and use your network data quickly, make sure to take a look at this service.

3.     IBM QRadar

Last but not least, IBM’s QRadar is also a comprehensive tool that contains integrated solutions for big data security analytics. While the infrastructure is pretty massive and includes many services, it gets complex if your setup isn’t huge.

How To Pursue a Career in Big Data Security Analytics?

Now that we have discovered everything there is about big data security analytics, you might wonder how you could break into the field and what it takes to become a top-of-the-line big data security analyst.

Well, first off, having a good grasp of cyber security concepts is the foremost thing you should concentrate on. Without a solid understanding of cyber security and the steps you need to take against possible attacks, you could never become a good big data security analyst, no matter how good you are with all the big data techniques.

So, make sure your number 1 priority is getting to learn about cyber security. If you want to pursue a degree, having a cyber security major with a minor in big data is undoubtedly the best combo. But, it’s not necessary. Having basic concepts of cyber security and big data or having a simple CS degree will also do since there are a lot of courses and bootcamps on the internet you could refer to.

According to statistics, around 55.5% of data security analysts have a bachelor’s degree, and approximately 12.1% have a master’s degree. So, you see, even with a high school degree, you could become a big data security analyst only if you have the skills required.

Big Data Security Analytics
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Wrap Up

After giving this a read, you may have come to realize how important big data analytics has become for businesses by not just looking at the benefits it provides but also how pivotal it has become. It provides a solution to all business issues that may develop.

The various types of big data analytics allow companies to process and utilize the massive amounts of raw data they collect daily. Therefore, big data security analytics may just be the forerunner catalyst in data and security development in the years to come.

Lastly, we saw some of the most valuable tools being used in the market for big data security analytics and what sets them apart, along with a career path for becoming a big data security analyst.

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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