Cloud and Generative Artificial Intelligence Security Architectures

Cloud and generative artificial intelligence (AI) represent a pivotal convergence of AI technologies and cloud-hosted services, effectively addressing the unique challenges inherent in these rapidly evolving fields. In cloud computing, security architectures are crafted to protect data, applications, and infrastructure from unauthorized access and vulnerabilities, while ensuring adherence to industry standards and regulatory requirements. The integration of generative AI adds further complexity to the security landscape, necessitating the implementation of AI-specific safeguards to protect against model vulnerabilities, prompt injection, data manipulation, and ethical concerns. This course provides an in-depth exploration of security architectural principles, design patterns, and best practices specifically tailored to the dynamic realm of cloud and generative AI services. Beginning with a comprehensive foundational overview, it delves into essential concepts and techniques crucial for designing, implementing, delivering, and managing secure cloud-based multi-tier applications and generative AI services. This includes multi-tenant networks, containers, micro-services, event-driven models, and the integration of generative AI services, utilizing foundational large language models(LLMs), retrieval augmented generation (RAG) AI knowledge bases, and AI agents. The curriculum emphasizes risk assessment and management associated with cloud and responsible AI adoption, understanding hybrid cloud network topologies, ensuring data protection through applied cryptography, and implementing end-to-end identity management, access control, monitoring, auditing, intrusion detection, incident response processes, fraud detection, and compliance with industry and regulatory standards. Upon completion, students are well-prepared to apply cloud and generative AI-based security architectures effectively.