Advancing Cloud Security in Digital Finance: AI-Driven Threat Detection, Cryptographic Solutions, and Privacy Challenges

Abstract

This study examines security risks, emerging technologies, and cryptographic techniques in cloud-based digital currency transactions using a quantitative research approach. Data was sourced from the REKT Database, Web3 Security Report, and Elliptic Open Dataset, employing descriptive statistical analysis, regression modeling, and time-series analysis to assess security vulnerabilities, fraud reduction trends, and regulatory compliance effectiveness. Findings reveal that AI-driven security measures reduced fraud cases by 55% from 2022 to 2025, while illicit transactions declined from 12.5% in 2019 to 6.1% in 2023, demonstrating the impact of cryptographic advancements and regulatory interventions. However, cybercriminals are shifting toward high-value, precision-based attacks, necessitating an integrated security framework. This study contributes to AI-driven security, cryptographic resilience, and regulatory compliance in cloud-based digital transactions. By analyzing emerging threats and security frameworks, it offers valuable insights for researchers, policymakers, and financial institutions. The findings aid in developing robust fraud detection, post-quantum cryptography, and regulatory strategies to enhance digital financial security. This research bridges technological advancements with compliance measures, ensuring a more resilient and secure cloud-based cryptocurrency ecosystem. The study recommends enhancing AI fraud detection with cryptographic security models, adopting post-quantum cryptography, strengthening regulatory compliance, and implementing zero-trust security models to ensure long-term resilience in cloud-based financial ecosystems.