Cloud Guardian: An AI-Assisted Edge-Based AWS Audit and Optimization System

Abstract: Cloud providers like Amazon Web Services (AWS) are easy to deploy and operate applications on, but at the same time, they are difficult to keep track of costs and security. It is found that some users have been inadvertently keeping idle virtual machines, unattached storage volumes and outdated snapshots in their accounts, resulting in wastage. Besides, security misconfigure like ports opened to internet can lead to security risks. It is impossible to identify these issues manually since most cloud infrastructure is made up of complex structure of interrelated resources and huge amount of configuration data. This paper proposes a cloud audit and optimization tool Cloud Guardian to address this problem, which is a command line-based tool that performs cloud audit on user’s AWS infrastructure, with the aim of identifying the most expensive and insecure resources. Cloud Guardian performs cloud infrastructure audit by collecting resource data, analyzing resource utilization, and using a rule- based analysis approach to flagged resource inefficiency and insecurity. In addition, Cloud Guardian also calculates a health score of the cloud account to make the analysis results more understandable. To make the analysis results more understandable, an AI advisor module is also built into the tool to explain the problems and suggest the solutions.

Keywords: Cloud Computing, AWS, Cloud Governance, Cost Optimization, Security Analysis, Cloud Audit, Command-Line Interface (CLI), Rule-Based Analysis, AI Advisory Systems, Cloud Resource Monitoring, Health Scoring, Edge Computing, ESP32, Credential Isolation, DevOps Automation, Cloud Security, Infrastructure Optimization.