A Risk-Aware Generative AI and LLM-Driven Cloud Framework for Secure Banking with PII Protection and Privacy Analytics in 5G Web Applications
DOI:
https://doi.org/10.15662/IJRAI.2022.0506025Keywords:
Generative AI, , Large Language Models, secure banking, , personally identifiable information, , privacy analytics, 5G web applications, cloud computing, cybersecurity,, data protection, AI governance, zero-trust security, financial technologyAbstract
The rapid evolution of digital banking, cloud-native systems, and 5G-enabled web applications has increased the need for secure, privacy-aware, and intelligent financial platforms. At the same time, the widespread use of Generative Artificial Intelligence (AI) and Large Language Models (LLMs) has created new opportunities for advanced analytics, automation, and customer engagement. However, the integration of these technologies also introduces risks related to personally identifiable information (PII) exposure, data misuse, and regulatory non-compliance. This paper proposes a risk-aware Generative AI and LLM-driven cloud framework designed to support secure banking operations while ensuring PII protection and privacy analytics in 5G web environments.The framework integrates cloud-native infrastructure, real-time data pipelines, and LLM-based decision intelligence with a multi-layered privacy and security architecture. It incorporates encryption, anonymization, differential privacy techniques, and zero-trust access control to safeguard sensitive financial and customer data. A privacy analytics layer enables continuous monitoring of PII usage, consent management, and regulatory compliance with standards such as GDPR and financial data protection policies. Generative AI models support fraud detection, customer support automation, and predictive analytics while operating under strict governance policies to prevent data leakage and model bias
The proposed architecture was evaluated using simulated banking transaction datasets and privacy-sensitive user interaction data. Results demonstrate improved detection of anomalous activities, enhanced privacy risk visibility, and reduced latency in 5G-enabled cloud environments. The integration of LLM-driven analytics with privacy-aware controls improved decision-making accuracy while maintaining data confidentiality and regulatory compliance. The study highlights the importance of combining risk-aware AI governance, privacy analytics, and secure cloud infrastructure to build trustworthy digital banking ecosystems. The framework provides a scalable and adaptable approach for financial institutions seeking to leverage generative AI while protecting sensitive customer information in next-generation web applications
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