Abstract:
Purpose: With an emphasis on ChatGPT specifically, this study attempts to look into
data protection measures in AI-driven conversational models. As artificial intelligence
(AI) technology become more ubiquitous in daily life, worries regarding data security
and privacy have grown. The study aims to evaluate ChatGPT's present data protection
practices, spot potential dangers and weaknesses, and suggest solutions that fit
changing user expectations, regulatory requirements, and ethical standards. The main
objective is to ensure privacy in the development and application of conversational AI
models by bridging the gap between technical breakthroughs and ethical issues.
Design/Methodology/Approach: A thorough approach for reviewing the literature
was used, looking at academic studies, industry reports, and legislative frameworks
pertaining to cybersecurity, data privacy, and AI ethics. With a focus on ChatGPT-like
models, the review summarized findings from earlier research on data safety in
conversational AI. The study evaluated the benefits and drawbacks of the state-of-the
art data protection procedures and pinpointed research needs by critically examining
the literature. In order to guarantee compliance, the investigation also looked at
regulatory requirements like GDPR in relation to AI-driven dialogues.
Findings: The analysis of the literature showed that even with ChatGPT’s many data
protection features, there are still a number of serious weaknesses, especially when it
comes to handling dynamic chats and retaining user data. The main dangers that have
been identified include inadequate user control over personal data, inadequate
openness in data handling, and unauthorized access to sensitive information. The
study also revealed shortcomings in user education about privacy procedures. The
report suggested a number of improved approaches to deal with these problems, such
as stronger encryption, increased data usage transparency, and better user education
initiatives.Practical Implications: The study provides stakeholders, legislators, and AI
developers with useful suggestions. Through the identification of weaknesses in
current data protection protocols, the study offers a path forward for enhancing
conversational AI privacy and security protocols. The suggested tactics, which include
improved encryption procedures, adherence to changing regulatory requirements, and
improved user training, can assist developers in building AI models that are more
private-focused and safe. These results also aid in the development of regulatory
frameworks that guarantee the appropriate application of AI while protecting user
privacy and confidence.
Originality/Value: This work contributes to the literature by concentrating on the data
privacy issues that conversational AI models such as ChatGPT face. Although data
privacy and AI ethics are extensively researched, this study tackles the particular
issues associated with AI-driven dialogues and suggests customized remedies. The
study's conclusions offer insightful information to audiences in academia and
business, laying the groundwork for further investigation and advancement in the safe
application of conversational AI.
