By Zach Amos
As the threat of cybercrime grows each year, cybersecurity must start leveraging artificial intelligence tools to combat digital threats more effectively.
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AI has become a powerful weapon, but there are concerns that it is too effective compared to human cybersecurity professionals, which can lead to dismissals and replacements.
Generation AI Wildcard
The latest breakthroughs in artificial intelligence technology are machine learning and generative AI. Unlike traditional AI, machine learning is not limited to analytics only, but can learn to act on datasets and make accurate predictions.
Machine learning programs use highly complex algorithms to learn from datasets. In addition to analyzing data, you can also use that data to observe patterns. Just like humans, they use what they learn to “visualize” models and act on them.
Programs that can take datasets and act independently have great potential in cybersecurity. Generative AI can search for patterns in code and identify the most common forms of cyberattacks. Instead of alerting human administrators to deal with the problem, the program can eliminate the threat itself.
need a human touch
Machine learning’s greatest strength is its adaptability. The more data we collect, the more we learn and the more threats we can stop. However, this does not mean that the technology is foolproof. The capabilities of machine learning programs depend on the amount of data available.
That is why the role of cybersecurity professionals remains important. Machine learning requires a human operator to teach the program how to use relevant data. It also requires human oversight in case the program makes a mistake. Machine learning alone is not powerful enough to stop all determined hackers. But when machine learning and human experts work together, they are a formidable force.
The benefits of machine learning programs for cybersecurity professionals are potentially enormous. Security programs that can tighten security to some extent rather than simply analyzing data can reduce workload and free up expertise.
Fatigue recovery
Cybersecurity has become an integral part of everyday life, but staying on top of all the latest trends, policies and programs can be difficult. This is especially true for cybersecurity professionals whose job it is to stay vigilant against threats.
These professionals are constantly bombarded with warnings and information about potential security breaches. Some of these alerts may be false. For example, the system flagged as a potential threat but was not confirmed, or it was an error.
Human error can occur if cybersecurity fatigue is not addressed. If you don’t check alerts properly, you risk infiltrating your system with real threat actors. Machine learning and AI tools can help reduce the margin of error by automating mundane tasks.
Generative AI tools can learn the most common causes of false alarms and how to confirm them. When such an alert is displayed, the AI tool can check the reason by itself and report it to the administrator. This process significantly reduces the workload of cybersecurity professionals, giving them time to address more important issues.
Machine learning tools are a powerful weapon against cyberthreats, but they require cybersecurity experts to use them properly. The power of generative AI tools in the hands of security professionals can defeat any cyberattack.
About the essayist: Zac Amos writes about cybersecurity and the technology industry and is a feature editor at: rehack.follow him twitter again LinkedIn More articles on emerging cybersecurity trends can be found here.
June 5, 2023
