As Generative AI becomes more integrated into various applications, establishing Digital Trust is essential. The rapid advancement of AI technologies brings both opportunities and challenges, such as intellectual property concerns and biases. Without trust in these systems, user adoption and in- novation could be significantly hindered, and businesses could miss boat to market leaders.

This research involved an in-depth literature analysis, examining key aspects of Digital Trust, such as (a) security, (b) privacy, (c) transparency and (d) ethics. The study analysed current academic and industry perspectives, focusing on how these elements interact with Generative AI. The findings were synthesized into a comprehensive framework to guide future developments in Digital Trust for AI systems.

The thesis identifies four critical pillars of Digital Trust in Generative AI: (a) security and reliability, (b) privacy and control, (c) transparency and accessibility, and (d) ethics and responsibility. These elements are crucial for fostering trust, ensuring that AI systems are secure, reliable, and ethically sound. By applying this framework, organizations can enhance their Digital Trust strategies, leading to greater user confidence, competitive advantage, and operational excellence. The research also provides insights into regulatory impacts and future technological advancements, emphasizing the need for continuous adaptation. For individuals, businesses and institutions focused on Digital Trust, this framework offers actionable guidance to navigate the evolving AI landscape and maintain trust in digital systems.