Machine learning-based security solutions differ from what is commonly thought of as this representative of the AI family. Nonetheless, ML is among the most potent AI cybersecurity tools we have to date. This technology uses data patterns to determine the probability that an event will (or will not) occur.
In a way, machine learning is the opposite of real Artificial Intelligence. Machine learning is all about “accuracy” and less about “success.” This means that ML tries to learn from a task-oriented data set. The aim is to find the best possible solution for the task at hand. For this purpose, the only viable solution is sought from the available data, even if it is not ideal. However, ML does not interpret data, so humans’ final decision still rests.
The ideal role for AI in cybersecurity is to interpret patterns identified by ML algorithms. Of course, modern AI cannot interpret results to the same extent as a human being. Although work is already being done on this human-like framework, true AI is still a long way off. After all, machines would have to transfer abstract concepts to different situations to reinterpret them. In other words, we’re nowhere near as close to that level of creativity, and critical thinking as some AI enthusiasts would like you to believe.
The benefits of machine learning are particularly evident in particularly tedious tasks, such as recognizing and adapting data patterns. Humans are not well suited to such jobs because they tire easily and dislike monotony. So while the interpretation of data is still firmly in the hands of humans, machine learning can help put data into a readable, dissectible form. Machine learning takes several forms in cybersecurity, each offering its benefits:
In data classification, data points are assigned to specific categories based on predefined rules. Classifying these points is an essential building block for profiling an attack, vulnerability, or another aspect of predictive security. This makes data classification a necessary interface between machine learning and cybersecurity.
Data clustering involves classifying outliers together (“clustering”) using predetermined rules into data collections with common characteristics or unexpected properties. The result can, e.g., B. can be used in the analysis of attack data for which a system is not yet trained. Such clusters can be used to determine how an attack occurred and which vulnerabilities were exploited or exposed.
Recommendations For Action
Recommendations for action are an expression of the proactive properties of ML-based security systems. Based on behavioral patterns and previous decisions, recommendations are made on how best to react. Once again, it must be pointed out that this is not the intelligent decision of a genuinely autonomous AI. Instead, it is an adaptive system that establishes logical relationships between existing data points. This tool makes it much easier to determine what countermeasures should be taken to counter threats and mitigate risk.
By analyzing insights from previous data and new unknown data sets, completely new probabilities can be derived with a possible synthesis. In contrast to the above recommendations, this is more about the likelihood that an action or system state is comparable to similar situations in the past. This way of deriving future probabilities can pre-screen an organization’s systems for weaknesses.
Predictive forecasting is the process of the ML component that looks furthest into the future. This approach evaluates existing data sets to predict possible outcomes. These are then primarily used to develop new threat models, anti-fraud measures, or protection against data breaches. This approach is also the cornerstone for numerous predictive endpoint solutions.
Also Read: How Does Machine Learning Work?