As it is happening with so many different fields, machine learning is redefining cybersecurity, too. While today cybersecurity goes as far as using a virtual private network application on one’s Android as the main security tool or enabling two-factor authentication, in the future, it’s bound to develop to the utmost extent. What makes machine learning such a potent tool is that it can process vast amounts of data in seemingly no time at all. It would take humans countless lifetimes to sort through, analyze, and create a working strategy with such an amount. Best of all, it works 24/7 and can help protect your network from all kinds of threats including malware, phishing attacks, and other malicious activities.
In traditional machine learning models, the most important factor is accumulating as much data as possible. When it comes to cybersecurity, technical teams are given massive amounts of information daily. There is a nearly endless stream of security alerts, malfunctions, and things happening at every minute of the day. The problem most technicians have though is how to interpret and use this data in a way that will effectively boost your capability to deter threats.
To reduce it to its simplest terms, machine learning works by creating a given objective, feeding a program a large volume of information and then letting it figure out how to achieve the solution. Along the way, it gets better and more efficient at solving these tasks. It creates more and more optimal solutions that very few people would have ever been able to discover. Today’s latest tools put this power directly into the hands of security technicians who can set specific parameters and create tailored solutions for specific types of problems.
Even with a dedicated and professional team, there is only so many people can do at any given time. With machine learning tools, teams can analyze millions of different threats as they occur. This means they can rank them hierarchically to assess their actual risk level to apply the best course of action to remedy. This means the possibility to process threats more quickly, apply effective counteractions, and solve problems before they even occur.
In the globally interconnected world, threats can happen any time of day or night. With machine learning, you can create an “automated playbook” that immediately shuts down a threat, blocks traffic that comes from a suspicious IP, and ensures the threats stay as localized as possible.
But there’s more it can offer. More than anything, these tools create the perfect approach for a hybridized targeted threat strategy where automation solves basic, low-level threats. Moreover, more serious problems are then sent to the respective security team members who can apply a more nuanced solution to one of these problems.
Like all new technological developments, machine-learning provides a new avenue for both IT professionals as well as business leaders to address security threats. It also helps to learn how to better understand and implement AI-powered technologies into their company’s infrastructure. Some great examples of programs to get started with our Symantec’s Target Attack Analysis and Sophos Intercept X. They offer great user interfaces and are excellent ways to build familiarity with the technology.
For non-professionals, this is an opportunity to have better protection against threats quicker. This can be particularly important for SMEs who may not necessarily have fulltime IT staff in their company.
Each year, these tools are getting smarter and more capable of handling a broader scope of activities. This will significantly change cybersecurity as machine learning becomes a more integrated part of the total security package. This is now time for professionals to understand how the technology works and use this information to assess security threats.
The first place to begin is to conduct a current analysis of existing vulnerabilities within your internal network, external platform, as well as outreach like email marketing and social media campaigning. Once you have evaluated where there could be problems, it’s time to start figuring out the different ways to utilize machine-based learning tools to begin addressing these issues. It’s important to remember this technology won’t immediately solve all the problems. Instead, it works to help you find comprehensive solutions over time.
Over the next few years, machine learning is only going to evolve. Thus, professionals will need to go through the adaptation process. They will need to better grasp how this technology can help to understand the complete security picture. From user behavior to the scope of attacks and how this impacts a company’s bottom line. At the end of the day, machine-learning is a powerful tool that security professionals are going to be able to use to do their job more effectively. This will foster the growth and development of the increasingly digital world.
Alexia is the author at Research Snipers covering all technology news including Google, Apple, Android, Xiaomi, Huawei, Samsung News, and More.
