Baihe, MaAlgarni, Moneer Mohammed2025-07-162025https://hdl.handle.net/20.500.14154/75831This thesis explores the challenges and opportunities associated with adopting artificial intelligence (AI)-powered cybersecurity solutions in cloud computing environments. It analyzes how trust, transparency, and technical limitations influence organizational decisions, and provides insights from both quantitative and qualitative data collected through surveys and case studies. The research contributes practical strategies for improving AI adoption in security frameworks, especially in sectors handling sensitive cloud-based data.This research investigates the trust and adoption of AI-powered cybersecurity solutions in cloud computing environments. As organizations increasingly rely on cloud services, traditional security approaches fall short in addressing evolving cyber threats. AI-driven tools offer advanced threat detection, anomaly identification, and automated response capabilities. However, concerns about trust, transparency, technical complexity, and data privacy continue to hinder widespread adoption. This study employs a mixed-methods approach, combining surveys and case studies, to explore the key factors influencing trust in AI systems and the barriers to their implementation. The findings highlight the importance of explainable AI, third-party audits, and staff training in building confidence. The research concludes with practical recommendations to help organizations integrate AI into cloud security frameworks effectively.36enArtificial IntelligenceCybersecurityCloud ComputingTrust in AIAI AdoptionThreat DetectionExplainable AIData PrivacyAI in Cloud SecuritySecurity AutomationThesis