Cyber Security Analytics: What it is, Types, and How it Works

Data has become one of the most valuable commodities in today's technological world. Sensitive data like trade secrets and R&D research data hold significant value and, if leaked, could seriously damage an organisation’s business. That's precisely why it has become a target for hackers. And using a strategy with the help of Cyber Security for Business analytics can make all the difference in preventing threats and can reduce operational costs. 

Nowadays, hackers are using newer and more sophisticated tools for cyber-attacks and data breaches. It has become a cat-and-mouse game between businesses and these Hackers. According to Statista, more than 75 per cent of the firms in the west with more than 1000 employees are planning to raise their spending on Cyber Security. That goes to show you how important it is for them.     

Hackers are constantly amping up their attacks, and if organisations want to have an effective plan for dealing with Cyber Security threats, they need to incorporate Cyber Security and its Analytics into their strategy.  In this blog, we are going to take a look at Cyber Security and its Analytics and how it can help businesses to create a well-rounded strategy for Cyber Security.

Table of Contents

1) Let’s learn what is Cyber Security Analytics

2) 5 Different types of Security Analytics - Explained 

3) Let's find out how it works  

4) How can analytics in Cyber Security help Businesses  

5) Benefits of Security Analytics  

6) Conclusion 

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Let’s learn what is Cyber Security Analytics
 

Cyber Security Analytics is the process of collecting and analysing data to identify and provide a comprehensive solution to Cyber threats. It is a proactive approach that effectively provides an organisation security solution.   

Analytics for Cyber Security involves building timelines to effectively predict and detect threats as well as how to mitigate them. Moreover, it includes machine learning and behavioural data analytics to discover potential threats.   

3 Purposes of Security Analytics

There are three primary purposes for Security Analytics:   

1) Detecting threats: The primary purpose is to detect new threats that can compromise an organisation's security.   

2) Identify threat and its Level: Next step is to identify the type of threat and the level of seriousness.  

3) Mitigate the threat: The final and perhaps essential purpose is to devise a solution to mitigate the threat.   

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5 Different types of Security Analytics – Explained

There are five different types of Security Analytics, and each has its own merits and approach, let's take a look at them below:   

a) Data Analytics 

b) Predictive Analysis 

c) Big Data Analytics 

d) Information Security Analytics 

e) Machine Learning and Automation  

Data Analytics 

Data Analytics is a brilliant and unique approach that involves the analysis of data from the users or employees along with the data from the devices in the organisation. Data Analytics is the engine organisations use to detect anomalies.  

Benefits:  

Data Analytics converts raw data into trends and is useful for identifying and analysing trends and forecasting events based on data. We can use data analytics to make informed decisions. Using this data, we can develop an effective business model with the added benefits of reduced costs and optimised products and services.   

Predictive Analytics  

Predictive Analytics is the method of using statistical data and algorithms to model and predict future events. It uses techniques like data mining, machine learning and predictive Modelling to compare historical data with current data to make predictions.  

Benefits: 

1) It improves the security of an organisation by building prediction models that can tackle any potential threats 

2) Improves customer relationships by actively getting feedback from customers to look for areas of improvement  

3) Attracts more business and helps brings in more customers by analysing user behaviour data 

4) Improves profitability with its efficient operating model 

5) Allows better management of resources 

6) Using the power of AI and machine learning, we can improve overall business operations by building effective business modelling 

Big data analytics  

Big Data are large and complex data sets that grow continually, which makes it challenging to manage them on a traditional security system and might require dedicated systems to deal with sheer volume.    

Benefits: 

1) Identify Customer Demand: Using Big Data, we can create a predictive model to identify customer needs better and remove redundancies. This could be done by creating focus groups and surveys on social media.   

2) Effective Maintenance: Using Big Data, we can even identify when a product can malfunction using historical logs and error messages based on the year the product was made. Utilising this Data, we can better manage inventory and improve the servicing.   

3) Better User Experience: By gathering user data already available on social media, Surveys and forums to deliver more personalised offers.   

4) Effective Operation: Now that we know what exactly the customer wants and what type of personalised offers to send, we can optimise and improve the product and production technique using big data.   

Information security Analytics 

Information security analytics is the process of identifying anomalies using patterns and Big Data. It can find out any potential outside threats that are not found in traditional security systems. Besides that, it also includes data analytics not found in the Cyber Space.   

Benefits: 

1) It can detect and identify anomalies as well as help find out the appropriate response  

2) Helpful in getting regulatory compliance  

3) Has Improved forensics that offers proper insights into detecting attacks 

Machine learning and automation  

We can automate the entire analytics process by utilising the power of AI and Machine Learning (ML). It is necessary based on the complexity and volume of data, which will take forever for humans to process. Analytics in Cyber Security requires the ability to process huge volumes of data, which will be very difficult without the help of AI and automation.   

Benefits of machine learning: We can create reliable trend reports using machine learning. Also, there will be fewer errors in the data modelling and more accuracy.   

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Let's find out how it works

It identifies threats, as these analytics platforms are constantly monitoring and are always on the lookout for detecting new threats. To do this, it would use techniques like:   

1) Collection and aggregation of data using big data and machine learning 

2) After that, classification of the identified threat based on its seriousness by building a predictive model and generating a report  

3) Using this prediction model, we can then try to neutralise the threat and mitigate its impacts 

How can Analytics in Cyber Security help Businesses
 

Nowadays, there's an increased need for data security. Cyber Security Analytics is a one-stop security solution for an entire organisation. Not only that, but it is also a proactive method which gives an early advantage in detecting threats. It can not only help detect and resolve the current threats but also helps detect potential future threats and guides on how to resolve them using its prediction models. 

Since businesses can detect threats at the earliest stage, they have a greater chance of eliminating them before crawling into other systems in their network. Moreover, it allows them to further enhance their network security by studying the previous intrusion.  

Apart from this, it allows business owners to measure their security performance. Constantly monitoring can help them recognise patterns of when, where and how often intrusion happens. 

Benefits of Security Analytics

Let’s take a look at some of the benefits of using Security Analytics:  

1) Prevents unnecessary data loss: it’s a proactive approach that helps us prevent data loss by acting early. It uses behaviour modelling to detect anomalies and abnormal user behaviour.  

2) Increased database queries: Having a dedicated system for security analytics frees up more resources and accelerates the processing of database queries.  

3) Improved data transfer: Having a fast and responsive database improves data transfer.  

4) More accurate and fewer errors: Since it only involves Artificial Intelligence (AI) and machine learning to create its prediction model, it would be more accurate and have fewer errors.   

5) Increased productivity and decreased downtime: It can prevent threats at the earliest stage, reducing exposure and damage, leading to more productivity and less downtime. And reduced downtime results in increased productivity.   

6) Security and integrity of data: Gives better protection and early risk assessment, allowing companies to find threats and cyber-attacks soon.   

7) Prioritise threats: Not all threats are equal; some will have devastating impacts while others barely cause damage. And analytics in Cyber Security allows us to prioritise which threats need more focus and enables us to create alerts.   

8) Improved profitability: Since it provides a one-stop solution for an organisation's security needs, it significantly reduces operating costs and frees up more resources.   

9) Faster resolution: Super-fast resolution since it uses machine learning and big data.

Conclusion   

We hope you understand Cyber Security Analytics and how it can help businesses improve their security architecture. You would've also learned about some of the latest technologies like big data and machine learning.   

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