Security Considerations in AI, Cloud Computing, and Edge Ecosystems

Abstract

The advancement of Artificial Intelligence (AI), cloud computing and edge ecosystems have evolved at an incredible speed and the solution that they provide Is highly efficient, scalable and responsive. However, this integration comes with several cross-cutting security risks that affect the capability, secrecy and assessability of systems and information. This paper discusses key concerns of security within these related fields and addresses specific factors which remain relevant to each domain, including adversarial attacks in artificial intelligence, data leakage in cloud computing, and device-level threats in edge computing. Further, the emerging threats as well as the processes cover issues such as secure data transmission risks and compliance, with suggested solutions being as follows: strong encryption methods suggested by adversarial training and secure infrastructure. Thus, the paper draws from real-life examples and developments, to argue and persuade the need for a strategic and tiered security model to protect these evolving technologies. This work can be used as a reference for practising scholars, users, and policy makers as a source of important guidance on how best to secure AI, cloud and edge systems in an ever-evolving digital environment.