Case Studies and Applications of Generative AI in Real-World Cybersecurity Scenarios

Abstract

This chapter elucidates what the major effects of generative artificial intelligence are: It changes things a lot. It first discusses the overall of what generative AI can do using one kind of generative AI, then it considers what generative AI does to make our defences against new things that can go wrong much more effective, and then it reflects on the major effects of generative AI on detecting malware and things that are subtly making our networks vulnerable. It emphasizes generative AI's ability to detect malware that was created to be hard to see with many examples of real things that attempt led to infection. Then it continues to discuss how generative AI interacts with threat intelligence feeds. It brings up that it is a way we can find out about cyber-attacks before they try to intrude. It goes on to relate to meaning how generative AI helps us with behavioural analysis and user authentication. It explains how we can protect privacy when learning about things that can go wrong using networks and threats. It focuses on collaborative learning and differential privacy. The next is to do with how well generative AI systems can handle ‘attacks' – what professionals call adversarial attacks. It wonders if they are scalable for cybersecurity, and lastly it stretches discussion on ethical and legal concerns. In conclusion this chapter suggests Generative AI has potential benefits which can be tapped for better security and privacy of the seamless digital devices connected online apart from it the chapter suggests collaboration among all the stakeholders connected to this network for good using better defense mechanisms which Gen AI can provide against intrusions and anomalies that can infect our networks. At the end this chapter wind up the discussion concluding that this chapter is aimed at specialists working in cybersecurity, researchers, and policymakers.