Cloud AI Integration Vulnerabilities in Large-Scale telecommunication projects: Securing multi-tenant environments without geographic boundaries

Cloud-based artificial intelligence systems are becoming more and more frequent in large-scale telecommunication projects in a bid to offer superior services in multi-tenant settings. The systems do not have conventional geographical boundaries and this presents peculiar security dilemmas. This study examined dangers presented by the integration of the cloud AI with telecommunication infrastructure. The researchers explored the vulnerabilities of authentication, data isolation breaches, and adversarial attacks of AI models. In a systematic literature review, 24 peer-reviewed publications of 2005 to 2022 were analyzed. It was discovered that 67% of multi-tenant cloud environments had at least one security incident a year. By deploying zero-trust architectures in combination with federated learning, the exposure of vulnerability was cut by 43%. Regression analysis showed that AI-based implementations of monitoring and threat detection efficacy were significantly correlated (R²=0.78, p= 0.001). The study postulated a detailed security architecture that combined blockchain-based threat identification, reinforcement learning with resource allocation, and generative adversarial network with threat intelligence. Findings revealed that the number of security breaches in the organizations that had this framework in place decreased by 56% in 12 months. The research offers viable suggestions on how to secure geographically distributed telecommunication systems by use of cloud AI technologies.