BUILDING TRUSTWORTHY AI SYSTEMS FOR CLOUD ENGINEERING: ENSURING RELIABILITY, SECURITY, AND DATA PROTECTION

The rapid advancement of artificial intelligence (AI) in cloud engineering presents both opportunities and challenges in building trustworthy systems.This article examines the critical principles that underpin trustworthy AI, emphasizing the importance of transparency, fairness, accountability, and data privacy.As organizations increasingly rely on cloud services for operational efficiency, the integration of AI technologies must align with stringent data protection regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).Through a series of case studies, this article illustrates successful implementations of AI in cloud environments that have enhanced security and compliance while fostering user trust.Additionally, it addresses the inherent risks associated with AI in cloud computing, including data privacy concerns, algorithmic biases, and unauthorized access to sensitive information.By proposing best practices and strategies for mitigating these risks, this article aims to provide a comprehensive framework for developing AI systems that are not only reliable and secure but also prioritize user data protection and ethical considerations.Ultimately, establishing trustworthy AI systems is essential for maintaining the integrity and safety of cloud engineering applications, ensuring that users can confidently leverage AI technologies.