ENTERPRISE FINANCIAL DATA SHARING AND SECURITY IN HYBRID CLOUD ENVIRONMENTS: AN INFORMATION FUSION APPROACH FOR BANKING SECTORS

Abstract

This paper explores a secure financial data sharing framework in hybrid cloud environments,
specifically designed for the banking sector. The integration of information fusion techniques with AI and machine
learning (ML) ensures real-time data accuracy, risk mitigation, and compliance with industry regulations. The
hybrid cloud approach utilizes both public and private cloud infrastructures to enhance security while optimizing
operational efficiency. Through comprehensive data fusion and validation, the system ensures secure data
transmission and protection of sensitive financial information across various cloud platforms.
Objectives: The objectives of this study are to develop a robust system for secure financial data sharing across
hybrid cloud platforms, ensuring data privacy during transmission between public and private cloud
environments. By leveraging information fusion, the system aims to enhance decision-making, fraud detection,
and risk management. Additionally, the framework seeks to ensure compliance with global regulations such as
GDPR and Basel III, while improving operational efficiency. The integration of AI and ML is critical to reducing
latency and enabling real-time processing, optimizing the overall performance of financial data sharing in hybrid
cloud environments.
Methods: The proposed system integrates information fusion, AI, and ML algorithms to enhance data accuracy
and threat detection. Secure protocols were implemented to ensure data safety and regulatory compliance.
Results: The framework achieved high data accuracy (99.85%), significantly reduced security risks (0.87 risk
score), and maintained low latency (1.56 seconds), ensuring efficient real-time data processing.
Conclusion: The proposed method enhances secure data sharing across hybrid cloud environments, balancing
security and efficiency while adhering to regulatory standards.