This research paper aims to present practical applications of Generative AI (GAI) within the cybersecurity field. Cyber threats are becoming more common, more advanced, and more dangerous every day. This threat landscape is always changing, which makes it hard for businesses and security experts to find better ways to deal with these threats. GAI technology gives them a good way to deal with these problems automatically and more efficiently over time. It lets them focus on more important security issues that need human help, while GAI systems handle general threat situations. Also, GAI systems are better at finding new malware and dangerous situations than people are. Using this feature of GAI can make the security system more stable. This idea has inspired many big tech companies, such as Google and Microsoft, to add parts of GAI to their cybersecurity systems to make them better at dealing with threats that are always changing. There are now a lot of cybersecurity tools that use GAI to make it easier and stronger to deal with new cybersecurity threats. Some of these tools are Google Cloud Security AI Workbench, Microsoft Security Copilot, and Sentinel One Purple AI. As GAI becomes more common in cybersecurity, it's important to remember that these systems have their own problems and limits. This paper also talks about some of GAI's problems, such as giving wrong results from time to time, being expensive to train, and the possibility that bad people will use it for illegal activities.
Kumar et al. (Thu,) studied this question.
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