Business security What does Artificial Intelligence have to do with Cyber ​​Security?

With digitalization, the explosion of connected devices and the Internet of Things, cyber security experts have their work cut out. More connected devices means more traffic, more attack vectors, more security breach attempts, and a lot more data that needs to be analyzed. The future will make this situation even more complex.
Artificial intelligence and machine learning are being applied more and more widely in different sectors and applications, and also in cyber security. When we talk about Artificial Intelligence (AI), we are referring to a broad concept of machines that are able to mimic cognitive functions and perform actions such as classification, anomaly detection, or grouping of samples to solve a problem effectively in a very similar way. to how a human being would do. Machine learning, on the other hand, could be considered an application or materialization of AI that is based on the idea that we can give access to data to machines and use algorithms that allow machines to self-learn how to solve problems from data.

For decades, a large number of machine learning algorithms have been presented in the scientific literature and have also been used in applications around us. While most AI solutions remain limited to their purpose, as they focus on a specific problem (the “what”) instead of trying to mimic the breadth of human cognitive functionality (the “how”), there is a undeniable evidence of the effectiveness of such solutions. From the recent victory of AlphaGo, to self-driving cars and movie suggestion engines, AI applications are already more efficient than humans in many scenarios when a specific task is established and enough data is provided.


AI is nothing new in cyber security. In fact, we’ve been using machine learning techniques since 2005 for tasks like sample analysis and categorization, URL reputation and categorization, and client-side detection logic. AI helps us quickly identify and analyze new exploits and weaknesses to support us in mitigating further attacks and is an integral part of our solutions.

In addition to improving prevention measures, AI techniques are essential for detecting violations and allow you to react even towards threats not yet known. In many cases, humans have been too slow to stop cyber attacks in time. AI systems that are designed to learn and adapt, and that are able to recognize even the smallest change in an environment, have the ability to act much faster – and rely on far more data than humans do when it also involves detecting new types of cyber attacks.

Machine learning algorithms can be used to create profiles of normal behavior, and these profiles can be even more global, or alternatively based on the user or the host. Based on this, it is possible to differentiate normal or abnormal behavior practically in real time. In the case of the Rapid Detection Service, for example, F-Secure constantly collects data with sensors on endpoints, and models it to find user- or host-based discrepancies to identify suspicious behavior on networks. All alarm signals are then sent to security experts who investigate incidents 24/7 by alerting customers if those alarms are valid. With AI, you can eliminate background noise and prioritize our experts’ time to investigate and respond to real threats.

But using profiles alone is not the best solution, especially if we let machines learn totally unsupervised, because this could result in attackers taking advantage of the fact that algorithms learn from behavioral patterns. Hence, we must strive to model a higher level of cognitive function by combining expert knowledge, such as known attack patterns, with self-learning profiles, and ensure that our system is resilient even to attacks against its adaptive nature. .

Combining man and machine

Investing in artificial intelligence doesn’t mean cutting humans out entirely. The ability to analyze and knowledge of humans is essential to determine the depth of an identified threat and to determine how to react to a specific scenario, and to be able to provide a higher level image and work together with AI to find the optimal solution. And we shouldn’t forget the other aspect of automation value, besides working as a part of the current detection system. AI has the ability to take some of the pressure off the “human” experts on certain fronts. AI can also be used to power data-driven tools to make our experts’ work much more efficient.

The human element is essential to the development of AI solutions valid in cyber security. AI needs human interaction and “training” to continue learning and improving, correcting false positives, and detecting the innovations of cybercriminals, as well as adapting learning algorithms to our domain of the problem. Man and machine must work together. While we employ AI in many production systems already, we are working against skilled counterparts who are doing their best to go undetected. This means that our approach needs to evolve over time, and that we must continue to evolve our AI-based systems to get better at preventing and detecting threats in time.

F-Secure calls this Live Security approach .


There is great potential in the market for creating technology that ensures that solutions are contextual and more proactive than reactive. Traditional, rules-based security is no longer enough: companies must protect their networks even against unknown threats, not just known ones. We believe the best approach for businesses to thrive is to bring together the best of both worlds. Combining the human experience with a machine learning system provides better results than using either just the machine or just the human factor.

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