Intelligent Cloud Framework for Privacy-Aware Financial Risk Assessment and Cybersecurity Resilience

Abstract

The increasing adoption of cloud computing in financial institutions has created opportunities for scalable risk analytics while simultaneously introducing significant challenges related to data privacy, cybersecurity, regulatory compliance, and operational resilience. Financial organizations process highly sensitive information across distributed cloud environments, making traditional risk assessment and security mechanisms insufficient for detecting evolving threats and protecting confidential data. This research proposes an Intelligent Cloud Framework for Privacy-Aware Financial Risk Assessment and Cybersecurity Resilience that integrates artificial intelligence, machine learning, privacy-preserving analytics, continuous threat monitoring, and adaptive risk management within a secure cloud architecture. The proposed framework combines financial risk indicators, transaction behavior, cybersecurity events, identity information, and infrastructure telemetry to develop dynamic risk profiles. Privacy-aware mechanisms, including data minimization, encryption, access control, federated learning, and differential privacy, are incorporated to reduce unnecessary exposure of sensitive information. Machine learning models are employed to identify anomalous financial behavior and cyber threats, while explainable AI supports transparent risk decisions. A continuous resilience layer enables adaptive response, recovery, and risk recalibration when security incidents occur. The framework is designed to provide scalable, intelligent, and privacy-conscious financial risk assessment while strengthening cybersecurity resilience across cloud-based financial ecosystems. The expected contribution is an integrated architecture that balances predictive intelligence, privacy protection, regulatory governance, and operational continuity.