Effective application of machine learning


Artificial intelligence and machine learning are now very popular in high-tech fields and this is not surprising as many companies are beginning to automate a growing number of their operations. Sometimes this leads to remarkable results, especially when it helps cybersecurity professionals automate repetitive tasks and put more emphasis on higher-level analysis.
Currently, however, machine learning is more of a fashion trend than a useful cybersecurity tool that still automates some tasks to keep your organization safe.
Effective application of machine learning
Machine learning technology, at its core, is designed to enable cybersecurity companies to predict the nature of future attacks based on data from attacks that have already occurred, much like Netflix makes recommendations based on what you've looked at. past.
According to Jack Gold, president and chief analyst at J. Gold Associates, the innovation is helping cybersecurity companies move away from a signature-based system to detect vulnerabilities. Instead, many companies are choosing machine learning tools for broader analysis of past events and collecting data from different sources.
For example, some solutions based on machine learning have shown their effectiveness in solving the following tasks: detecting malicious activity, helping security personnel determine the tasks that they need to perform during the investigation, analyzing mobile devices, reducing the number of false alarms of security systems, automating repetitive tasks , and possible closure of some 0day vulnerabilities.
A number of tech giants, including Google, have recently invested heavily in tools that use machine learning to secure Android mobile devices. Amazon also acquired harvest.AI, a startup to better collect and analyze data residing on its S3 cloud storage server.
AI Limitations for Cybersecurity
Given the above, it is worth noting that the signal-to-noise ratio for automated threat events is currently not sufficient for most organizations. The fact is that automating these threats - or, in other words, automatically identifying threats - is difficult to accomplish within an organization because the threats, vulnerabilities and risks of each company are unique. Ultimately, machine learning can help cybersecurity professionals, but it cannot be a complete solution that can replace traditional systems.