Abstract
The adoption of multi cloud strategies across enterprise organizations has introduced a critical security challenge: the inconsistent management of user privileges across heterogeneous cloud platforms. Each major cloud provider, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), implements Identity and Access Management (IAM) under different architectures, resulting in privilege fragmentation, privilege creep, and widening security gaps that existing platform native tools cannot adequately address. This study presented the Sentinel Harmonization Engine, a Python based system designed to ingest IAM data from all three platforms, normalize user privileges into a unified HIGH, MEDIUM, and LOW classification scale, detect cross platform inconsistencies through an automated harmonization algorithm, and generate actionable remediation recommendations. Testing on a simulated multi cloud dataset identified seven distinct privilege inconsistencies and produced a Security Health Score of 50 out of 100, indicating a critical risk environment. The system is delivered through an interactive Streamlit dashboard with support for CSV and Excel report exports, email alert simulation, and scan history logging. The findings established that automated privilege harmonization across multi cloud environments is technically achievable using Python, and that the Sentinel Harmonization Engine provides a practical, extensible foundation for enterprise grade multi cloud Identity and Access Management governance.
Keywords: Multi Cloud Security, Identity and Access Management (IAM), Privilege Harmonization, Least Privilege Principle, Python Automation
Authors:
Udeagwu Chinedu Kelvin
Department of Cyber Security, Faculty of Computing
Air Force Institute of Technology (AFIT), Kaduna, Nigeria
Email: udeagwuchinedu579@gmail.com