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Data Science

How AI is shaping the future of cyber security

Learn the importance of data visualizations when telling compelling data stories, along with the common pitfalls faced by Business Analysts and Data Analysts.

Everyone by now will have heard the phrase, “It’s not AI that will take your job, but someone who knows how to use it!” 

Well, there are few areas where this is truer than in cyber security. The rapid evolution of machine learning and artificial intelligence has created both headaches and solutions for cyber security specialists. Forecasts predict a rise of $120 billion in the global market for AI-based cyber security products. 
 
As the technology continues to develop, the need to fully utilise its potential in the industry will only increase as the same AI software is used to illegally hack these systems. 

Written by

Adam is a Marketing Executive at Learning People, specialising in software development and data with a background in tech and project management.

Adam YuleMarketing Executive
Adam Yule

Why is AI so important in cyber security? 

The advantage of AI as a subset of machine learning, is its ability to analyse large datasets and find patterns. Information that might take hours or days for a human to navigate through and draw insights from, could take just minutes for an advanced machine learning system. 

This is all done with minimal interaction from a human being, allowing the machine learning programme to do the grunt work whilst the cyber security specialist can dedicate more of their time to problem-solving and prevention methods. 

However, the adaptive nature of AI means that there are even more tools beyond this, including simple reactive measures that can be initiated instantaneously in the event of a cyber security attack. 


 
So what does AI do, exactly? 

The question should probably be, what won’t it be doing? A recent research article by Science Direct identified five key areas; Identify, Protect, Detect, Respond & Recover. These cover a wide range of different elements, from governance and authentication to anomalies and mitigation. 

Currently, these are all surface level processes designed to make a cyber security specialist’s role easier. By dealing with some of the simpler aspects, it leaves the human element to concentrate on active improvement and defence of the systems. 

 

Identification 

A relatively simple yet time-consuming process; see where the risks are and determine how to prevent them being exploited. This could be over multiple systems, employees, partners and millions of data points, perfectly suited to a programme that learns as it goes.  

This information will then be used to define and enable risk management by the business or organisation, the foundations for everything else that comes next. 

 

Protection 

Expanding out from the identification principles, cyber security specialists can then train the AI software to implement appropriate controls limiting or containing the effects of a cyber security attack.  This would include authenticating users, devices and other assets, monitoring users to determine any anomalous behaviour, automated access control, integrity monitoring and control of data access.   

Artificial intelligence, through Machine Learning algorithms, will then also continue to develop further protection solutions to the ever-changing identification area. 

 

Detection 

Cyber security is a constantly evolving arena, where new technologies, systems, software and ways of thinking produce more and increasingly effective methods of attack. 

Training AI in the various detection methods, combined with providing it with as much data as possible to learn from, will give any cyber security team an edge. Early detection is the most important, as it will mitigate the disruption to services and allow it to be contained, an automated process that AI is also perfectly suited to.  

Continuous monitoring of internal and external factors for anomalies can prevent attacks altogether, stopping the intrusions before they’ve had chance to cause any damage. 

 

Response 

This is the first line of defense against an attack. Learning from the incident also allows for mitigation of similar attacks in the future by analyzing the incident and determining more efficient responses. This makes for faster containment and less disruption from the event. It further frees up time and focus for analysts to work on other areas and add their own creative input to the development process.  

 

Recover 

A quick return to normal processes is essential for a company to continue providing its services and operations. It also gives AI the opportunity to determine if anything further was affected and learn from the incident to repair damage faster. It is important at this time for the AI to be able to distil the data down to be interpreted by the cyber security analysts, allowing for future detection and faster responses, from planning frameworks, improvement and rapid aggregation of the necessary data. 

 

The Future of cyber security & AI 

AI in cyber security is a tale of two halves; for every benefit to AI, there is another use for AI undermining and attacking the same systems. The evolution of it on both fronts, including quantum computing and more interpretable AI models, will continue to rapidly progress and it’s essential for the future that the workforce is trained to be able to use it to its fullest potential.  
 
Interested in a career in cyber security or AI? Book a consultation with one of our expert consultants today and we’ll send you a free career guide ahead of your consultation. 

 

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