Artificial intelligence and machine learning are transforming the world of cybersecurity. ML-based technologies are increasingly helping to combat large-scale fraud, evaluate and optimize business processes, improve testing procedures, and develop new solutions to existing problems.
Data processing algorithms allow computer systems to perform selected tasks by identifying patterns and anomalies in large amounts of data, transforming complex data into a compact representation known as a model. Without the creation of true AI being its ultimate goal, machine learning is considered one of the technologies that could be the key to achieving it.
Machine learning applications in cybersecurity
Machine learning security solutions are different from the common perception of the AI family. They represent the most powerful artificial intelligence tools for cybersecurity we have to date. Within this technology, data models are used to reveal the probability of an event occurring.
Machine learning excels in activities such as identifying and adapting data models, helping humans to frame a large amount of data in a readable and analysis-ready presentation. Cybersecurity through machine learning takes place in different forms, each with its advantages:
Data classification
Data classification works through preset rules to assign categories to data points. Labeling these points is an important part of creating an attack, vulnerability, and other proactive security profiles. This is fundamental for the integration between machine learning and cybersecurity.
Clustering of data
Data clustering captures anomalies in the classification of predefined rules, including them in “clustered” data collections with shared aspects or occasional characteristics. For example, it can be used for the analysis of attack data for which a system has not yet undergone training. These clusters can help determine how an attack occurred, along with the exploited and exposed elements.
Recommended lines of action
Recommended interventions improve the proactive measures of a machine learning security system. These are warnings based on behavioral patterns and previous decisions that indicate recommended interventions naturally. It is important to reaffirm here that there is no intelligent decision-making through an autonomous artificial intelligence solution. Rather, it is a framework of adaptive conclusions that through pre-existing data points can reach conclusions about logical relationships. Threat responses and risk mitigation can be greatly supported by this type of tool.
Summary of possibilities
The synthesis of possibilities makes it possible to synthesize entirely new possibilities based on lessons learned from previous data and new sets of unknown data. It differs slightly from advice in that it focuses more on the probability that an action or the state of a system is in line with similar previous situations. For example, it can be used for a preventive investigation of weaknesses in an organization’s systems.
Prediction
This benefit is achieved by predicting potential outcomes through the evaluation of existing datasets. This process can be used primarily for creating threat models to define fraud prevention and data breach protection and is a staple of many endpoint-based predictive solutions.
Computer security through artificial intelligence, with support for machine learning capabilities, is set to become a powerful tool in the near future. We at arimas® strongly believe in technological possibilities and believe that the future lies in digital innovation and the development of these new technologies.
