Auto-Scalable Policy-Driven File Routing Using AI and Google Cloud Native Services

This study proposes an Artificial Intelligence (AI) based, scalable, and policy-driven file routing system that utilizes Cloud services through Google Cloud Native, leveraging the inefficiencies of conventional file movement frameworks.Using tools such as Google Cloud Storage, Eventarc, Pub/Sub, and Cloud Run in a Dockerized environment, the system enables the smart classification of files, dynamic policy analysis, and secure file transmission over various protocols.The quality of service packet routing optimizations is performed by using AI model functions to optimize routing decisions involving file metadata, network load, and security parameters, utilizing Random Forest, SVM, and Artificial Neural Networks.The architecture is auto-scaled with the help of Google Cloud's serverless infrastructure, which maximizes resource efficiency and accountability to changes in the workload.The criteria of accuracy, latency, resource utilization, and scalability will demonstrate the effectiveness of the AI-driven method.The given solution is an effective alternative to older systems, offering increased performance, compliance, and operational efficiency that can be beneficial in contemporary, data-driven businesses.