Sensitive Data Protection in Serverless Fintech World

Financial technology institutions face growing challenges in securing sensitive data within serverless architectures due to reduced infrastructure visibility, ephemeral processing environments, and complex regulatory requirements. This work presents an integrated framework that combines automation of sensitive data detection, classification, and access controls specifically tailored for serverless financial applications. Machine learning techniques enable real-time identification of sensitive information across distributed environments, while automated classification systems organize data according to regulatory and organizational policies. Intelligent access control mechanisms, including role-based and attribute-based models, provide enforcement capabilities adapted to ephemeral computing contexts. Cloud-native security implementations leverage provider services and event-driven architectures to create comprehensive protection throughout data lifecycles. Evaluation through case studies demonstrates significant improvements in security posture, compliance adherence, and operational efficiency compared to manual approaches. The integration of these automated components creates a cohesive security framework that addresses the unique challenges of protecting financial data in dynamic serverless environments.