Using Machine Learning to Evaluate Cybersecurity Risks

Cybersecurity is at the forefront of discussion for businesses attempting to protect themselves from an array of risks. As attackers develop more sophisticated techniques to bypass defensive measures, companies must take action and increase the level of computer security they have in place.

It starts by implementing necessary security precautions spanning both the tangible and digital assets of a business. Once the fundamentals are in place, an organization can utilize advanced techniques such as security information and event management, user and entity behavioral analytics, fault tree analysis and advanced machine learning.

Cybersecurity is pivotal to protecting and advancing a company’s well-being now more than ever. It also requires the application of rapidly-maturing technologies, such as machine learning.

What Is Machine Learning? 

Machine learning uses analytical algorithms to discover patterns in data, utilize these trends to assess a condition and identify whether there is a potential risk. This technology is a step towards true artificial intelligence in that it allows a system to perceive, understand, and act upon the information it receives. It also employs analytics rules to assess situations and classify irregularities.

The ultimate goal is to replicate human actions within systems and data while minimizing the risk attached to human error. The result of utilizing this method is additional opportunities for automation and greater peace of mind with cybersecurity.

How Does Machine Learning Support Cybersecurity? 

Although this approach is developing rapidly, it still does not replace human intelligence and problem-solving as a resource. However, it can reduce the likelihood of errors and faults in tasks. It identifies and addresses irregularities, vulnerabilities and threats quickly and efficiently. Machine learning identifies security hazards faster than any individual while also adding a layer of security and reducing the potential risk of human error.

Machine learning analyzes current and past data to identify possible weak points in a business’s cybersecurity perimeter. By pinpointing these risks, it aids information security in recognizing and resolving points of liability. This strategy also utilizes past and present information to find trends that are predictive of future occurrences. By reviewing data logs, AI finds suspicious activities and flags them as likely dangers, which cybersecurity professionals can then quarantine and investigate further.

As with most surveillance risks within a business, time plays a crucial role in this. Machine learning identifies future hazards and delivers timely alerts for events-in-progress so the organization can stop the threat before it is too late.

How to Identify Security Risks

Machine learning works best when organizations can outline the potential threats. However, it cannot currently input those threats itself. At this point, human intelligence and reasoning come into play. One of the best ways to assess risk is by using Fault Tree Analysis (FTA).

Fault Tree Analysis is a top-to-bottom deductive approach for analyzing safety issues within a business. By using probability, it determines the likelihood of an unwanted event. The output is a holistic view of undesirable results, vulnerabilities and problems a company may experience. Professionals can then input these “action items” into machine learning so it can actively monitor and assess the health of the organization’s systems.

How to Use Machine Learning for Cybersecurity 

It is an unrealistic expectation to enable this technology and then expect it to work on its own. Instead, it works best when handlers initially define the desired result and then confirm it so the system knows what to look for in the future. It then can reach the expected result consistently once it has the parameters to work within. This system has multiple uses in the cybersecurity industry:

  • Classification: The process of identifying and categorizing observable information into positive or negative indicators. 
  • Prediction: The process of reviewing past and present data to determine future breaches. 
  • Visualization: The process of evaluating and organizing large sets of figures into visual segments to classify irregularities and potential threats. 

When organizations use machine learning to bolster cybersecurity, they can address defined problems, integrate systems, access data to distinguish dangers, reduce the potential risk of error and allow frictionless audits of digital security measures. Most importantly, this technology provides time-sensitive responses to threats, saving businesses time and money.

Machine Learning for a Safer Tomorrow

As additional cybersecurity issues arise, machine learning will remain the most promising technology to resolve these problems. Moreover, the demand for machine learning products will continue to grow as the list of threats expands.

Kayla Matthews writes about cybersecurity and technology for publications like Malwarebytes, Security Boulevard, InformationWeek and CloudTweaks. To read more from Kayla, visit her blog: