The Chat Generative Pre-training Transformer (GPT), also known as ChatGPT, is a powerful generative AI model that can simulate human-like dialogues across a variety of domains. However, this popularity has attracted the attention of malicious actors who exploit ChatGPT to launch cyberattacks. This paper examines the tactics that adversaries use to leverage ChatGPT in a variety of cyberattacks. Attackers pose as regular users and manipulate ChatGPT’s vulnerability to malicious interactions, particularly in the context of cyber assault. The paper presents illustrative examples of cyberattacks that are possible with ChatGPT and discusses the realm of ChatGPT-fueled cybersecurity threats. The paper also investigates the extent of user awareness of the relationship between ChatGPT and cyberattacks. A survey of 253 participants was conducted, and their responses were measured on a three-point Likert scale. The results provide a comprehensive understanding of how ChatGPT can be used to improve business processes and identify areas for improvement. Over 80% of the participants agreed that cyber criminals use ChatGPT for malicious purposes. This finding underscores the importance of improving the security of this novel model. Organizations must take steps to protect their computational infrastructure. This analysis also highlights opportunities for streamlining processes, improving service quality, and increasing efficiency. Finally, the paper provides recommendations for using ChatGPT in a secure manner, outlining ways to mitigate potential cyberattacks and strengthen defenses against adversaries.

This paper presents two chatbot systems, ALICE and Elizabeth, illustrating the dialogue knowledge representation and pattern matching techniques of each. We discuss the problems which arise when using the Corpus of Spoken Afrikaans (Korpus Gesproke Afrikaans) to retrain the ALICE chatbot system with human dialogue examples. A Java program to convert from dialogue transcripts to the AIML linguistic knowledge representation formalism provides a basic implementation of corpus-based chatbot training. The Java program used the Afrikaans dialogue corpus texts to generate two versions of the Afrikaans chatbot.

The COVID-19 pandemic had a significant impact on many facets of human behavior. Most significantly, it required significant changes to how daily activities were conducted. The majority of individuals were forced to work and communicate from home due to social distancing, which opened the door for more virtual meetings. Since most organizations started allowing their employees to work from home, the majority of online meeting platforms—like Zoom and Microsoft Teams—have become more and more popular. Online meetings were not only used by businesses but also by educational institutes to carry out online learning, hospitals to conduct meetings and certain surgeries, and many other industries. The online meetings are practical and simple to organize, but their information security is not as high as that of conventional meetings. Security and privacy issues resulted from this. The safety of personal information such as names, contacts, and locations, the security of online recordings since sensitive information was discussed in most meetings, the security of data while it was in transit, and the potential for competitors to intercept your business were among the many security issues that were raised. Zoom, as one of the famous online conference systems, faced many global concerns, such as sharing private information with third parties, exposing users to unauthorized bullying calls, and adopting questionable end-to-end encryption processes. Additionally, companies have had virtual meetings hacked, leading to privacy issues since hackers can obtain data illegally. Therefore, this research proposes a security framework to address the privacy issues in the Zoom system by applying a set of governance and technical controls. The governance controls provide a strategic view of how an organization controls its security while implementing the technical controls avoids privacy issues in online conference systems. The proposed framework implements a set of technical controls such as encryption, auditing, authentication, and role-based access control. The results were promising and significantly addressed Zoom's privacy issues.

The Chat Generative Pre-training Transformer (GPT), also known as ChatGPT, is a powerful generative AI model that can simulate human-like dialogues across a variety of domains. However, this popularity has attracted the attention of malicious actors who exploit ChatGPT to launch cyberattacks. This paper examines the tactics that adversaries use to leverage ChatGPT in a variety of cyberattacks. Attackers pose as regular users and manipulate ChatGPT’s vulnerability to malicious interactions, particularly in the context of cyber assault. The paper presents illustrative examples of cyberattacks that are possible with ChatGPT and discusses the realm of ChatGPT-fueled cybersecurity threats. The paper also investigates the extent of user awareness of the relationship between ChatGPT and cyberattacks. A survey of 253 participants was conducted, and their responses were measured on a three-point Likert scale. The results provide a comprehensive understanding of how ChatGPT can be used to improve business processes and identify areas for improvement. Over 80% of the participants agreed that cyber criminals use ChatGPT for malicious purposes. This finding underscores the importance of improving the security of this novel model. Organizations must take steps to protect their computational infrastructure. This analysis also highlights opportunities for streamlining processes, improving service quality, and increasing efficiency. Finally, the paper provides recommendations for using ChatGPT in a secure manner, outlining ways to mitigate potential cyberattacks and strengthen defenses against adversaries.