One of the biggest artificial intelligence trends we’re seeing is the increased use of AI technology for cybersecurity and surveillance.
Many believe that the introduction of artificial intelligence in cybersecurity technology will be a kind of revolution and this will happen much sooner than one might think. In fact, in the future, we are likely to expect only gradual improvements in this area. But even these steps towards absolute autonomy still go far beyond our capabilities in the past.
When looking for new ways to apply machine learning and artificial intelligence in the field of cybersecurity, it is important to outline the range of modern problems in this area. AI technologies can be useful for improving many processes and aspects that we have long taken for granted.
A significant part of cybersecurity weaknesses is related to the human factor. For example, even with a large IT team, properly configuring a system can be an incredibly difficult task. Computer security is constantly improving, and today this area has become more complex than ever. Adaptive tools can help troubleshoot issues that arise when replacing, modifying, and upgrading network systems.
Manual labor efficiency is another cybersecurity issue. A manual process cannot be replicated exactly the same every time, especially in a dynamic environment such as today’s cybersecurity landscape. Customizing multiple corporate endpoints is one of the most time-consuming tasks. After initially provisioning a device, IT pros often have to go back to the device to fix configurations or update settings that can’t be changed remotely.
It also should not be forgotten that the nature of threats is constantly changing. If people are responsible for responding to them, their speed of action can be slowed down when faced with unexpected problems. A system based on AI and machine learning technologies can work under the same conditions with minimal delay.
Threat response time is one of the most important performance indicators of a cybersecurity service. Attacks are known to move very quickly from exploitation to deployment. In the past, before launching an attack, attackers had to manually check all vulnerabilities and disable security systems and sometimes this process could take weeks.
A person’s reaction may not be fast enough, even if the type of attack is well known. This is why many security teams are more focused on remediating successful attacks than preventing them. Undetected attacks represent a separate danger.
Machine learning technologies are able to extract attack data, group it and prepare it for analysis. They can provide reports to cybersecurity professionals to facilitate data processing and decision making. In addition to reports, this type of security system can also offer recommended actions to limit further damage and prevent further attacks.
Ideally, the role of AI in cybersecurity comes down to interpreting patterns discovered by machine learning algorithms. Of course, modern AI is not yet able to interpret the results as well as a human. This area is actively developing, a search is underway for algorithms similar to human thinking. But the creation of real AI is still far away. Machines have yet to learn how to rethink situations in abstract terms. Their creativity and ability to think critically is far from the popular image of ideal AI.
References:
https://www.computer.org/publications/tech-news/trends/the-use-of-artificial-intelligence-in-
cybersecurityhttps://www.weforum.org/agenda/2022/07/why-ai-is-the-key-to-cutting-edge-cybersecurity/