4 Ways in Which Data Science is Reforming Cyber Security Risk Assessment

Are you concerned about the increasing number of cyberattacks? No business is immune to such hacks but can take necessary precautions. Fortunately, data science has come in handy in cybersecurity risk assessment. Cybersecurity professionals are opting for the best data science programs online to gain knowledge about its contribution to their field. This post will tell you all about the relationship between cybersecurity risk assessment and data science. 


Learning of the Blog

  • Introduction
  • What is Data Science?
  • Relationship Between Data Science and Cyber Security
  • How Data Science is Reforming Cyber Security Risk Assessment
  • Bottom Line



We generate loads of data daily, so much, which is hard to imagine. But this data can be put to good use by drawing meaningful insights from it. Cybersecurity risk assessment is all about quantifying and minimizing the damage caused by cyber hacks. Thus, data generated from every online transaction can be utilized to strengthen cybersecurity. 


What is Data Science?

Data science is the method of analyzing structured or unstructured data. Companies hire data science experts to derive valuable insights from raw data to improve existing business processes or plan future endeavors. 


Relationship Between Data Science and Cyber Security

The simple meaning of data science is understanding raw data. It is useful to analyze large sets of information quickly and accurately to extract meaningful insights. Cybersecurity experts use Machine Learning (ML) to analyze data and draw conclusions from it. 


For example, organizations can understand from their network data that most security breaches occur during 2-3 AM in the night. A more detailed analysis may also help you identify the specific vulnerabilities, helping to secure the network. 


Machine Learning can also help in coming up with definite patterns of cyberattacks. Cybersecurity experts have to be a step ahead of hackers, predict attacks, and react quickly in case of an attack. 


How Data Science is Reforming Cyber Security Risk Assessment


Companies collect invaluable business data over the years. Cybersecurity businesses have already realized the significance of data and have started investing in data science to minimize the risk of hacks. With the increasing number of smartphones and IoT devices, businesses can gather large volumes of data about their customers; data security is a primary concern here. This is where data science and cybersecurity can go hand in hand. Below are the ways by which data science is reforming cybersecurity risk assessment. 



  • Associate Rule Learning (ARL) 

ARL is an approach where ML algorithms study the previous data, analyze its characteristics, and automatically generate an alert if any deviation is observed. Also, the system automatically updates itself to alert in case of any slightest variations. 




  • Monitoring Workflows 


 Data from most data breaches suggests that they were a result of ‘inside jobs.’ ‘Inside jobs’ means that the security was compromised because of careless activities from within the organization. Data science helps come up with techniques that prevent such incidents. 



One possible way is to restrict the access of data to employees who can access the data. The staff can be given specific login credentials to access the data; the HRD and system administrators can work together towards it. 



  • Use of ML 

Cyber Security organizations employ an ML-based network that learns and evolves with time (as more data is fed into the system) and understand the data storage and sharing pattern. The system can detect any unusual behavior, identify weak spots, and recommend various measures that can be taken to strengthen the security system. 



Organizations also take the help of their IT team and ethical hackers to design such systems that work automatically to identify loopholes that criminals can use to gain illegal access to the data. 



  • Fraud Detection 

 Depending upon the size and scope of the organization, companies create a model to identify frauds. As fraudulent transactions are rare as compared to general transactions, such models help point out the suspicious transactions. 



Bottom Line

With data becoming more and more critical to companies dealing with valuable information, cybersecurity professionals opt for data science training  courses to understand the use of data analytics. Companies should always strive towards keeping the data of their customers safe and stay a step ahead of highly creative and genius cybercriminals. 


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