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Generative artificial intelligence (GenAI) is a part of artificial intelligence which has the ability to generate content in various formats ranging from text to videos and images to audio formats. GenAI has the ability to inherently learn from large datasets and can produce results that can be of optimal use in case of cybersecurity. In the current digital landscape, we see a plethora of electronic gadgets connected to this seamless network of devices connected online. These seamless network of devices which were earlier unable to connect due to lack of ip addresses are now able to connect and are improving the quality of human life ranging from home appliances to health domain. From here we see emergence of smart networks which at one side is a boon but at the same time they have the risk of exploitation with unexpected cyberattacks. Hence, this chapter is an effort to highlight the issues concerning cyberthreats and advice on how GenAI can be utilized to mitigate these risks. This chapter focused on applying generative AI to secured IoT devices. By discussing the core concepts of IoT security, such as device authentication and access control, the chapter demonstrated how the next-generation generative AI models, including GANs and VAEs, can boost anomaly detection for device security. The chapter also provided examples of real-life use cases to illustrate how generative AI can be used to optimize the energy grid, protect data privacy, and strengthen cybersecurity efforts. Additionally, this chapter presented the key issues related to ethical considerations pertaining to privacy, bias, and accountability in the development and deployment of responsible AI. Moreover, it introduced the legal aspects of privacy legislation, data protection, and cybersecurity compliance. Finally, the chapter outlined some of the future trends in generative AI for IoT security to name a few are enhanced threat detection, privacy-preserving multimedia processing, and secure communications. The chapter then encourages organizations to start using generative AI to enable systems to become proactive about IoT security and reduce the massive onslaught of cyber threats while navigating an ever-evolving digital landscape.
Generative artificial intelligence (GenAI) is a part of artificial intelligence which has the ability to generate content in various formats ranging from text to videos and images to audio formats. GenAI has the ability to inherently learn from large datasets and can produce results that can be of optimal use in case of cybersecurity. In the current digital landscape, we see a plethora of electronic gadgets connected to this seamless network of devices connected online. These seamless network of devices which were earlier unable to connect due to lack of ip addresses are now able to connect and are improving the quality of human life ranging from home appliances to health domain. From here we see emergence of smart networks which at one side is a boon but at the same time they have the risk of exploitation with unexpected cyberattacks. Hence, this chapter is an effort to highlight the issues concerning cyberthreats and advice on how GenAI can be utilized to mitigate these risks. This chapter focused on applying generative AI to secured IoT devices. By discussing the core concepts of IoT security, such as device authentication and access control, the chapter demonstrated how the next-generation generative AI models, including GANs and VAEs, can boost anomaly detection for device security. The chapter also provided examples of real-life use cases to illustrate how generative AI can be used to optimize the energy grid, protect data privacy, and strengthen cybersecurity efforts. Additionally, this chapter presented the key issues related to ethical considerations pertaining to privacy, bias, and accountability in the development and deployment of responsible AI. Moreover, it introduced the legal aspects of privacy legislation, data protection, and cybersecurity compliance. Finally, the chapter outlined some of the future trends in generative AI for IoT security to name a few are enhanced threat detection, privacy-preserving multimedia processing, and secure communications. The chapter then encourages organizations to start using generative AI to enable systems to become proactive about IoT security and reduce the massive onslaught of cyber threats while navigating an ever-evolving digital landscape.
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