Data Security and Privacy in AI Driven Financial Platforms: Challenges and Solutions

This article examines the multifaceted challenges of securing AI driven financial platforms and presents a comprehensive framework of technical, organizational, and regulatory solutions. The financial sector has witnessed unprecedented transformation with the integration of artificial intelligence technologies, creating substantial economic value while introducing complex security and privacy concerns. Financial data represents one of the most sensitive categories of personal information, creating attractive targets for malicious actors. It analyzes advanced encryption methodologies including homomorphic encryption, secure multi party computation, and encrypted machine learning models that enable computation on protected data. Zero trust security architectures are explored as a paradigm for protecting AI systems through continuous authentication and authorization. The regulatory landscape for AI in finance is examined, highlighting key frameworks like GDPR, CCPA, and emerging AI specific regulations. Ethical considerations including algorithmic fairness and the tension between model explainability and performance are addressed alongside adversarial threats targeting financial AI systems. The article presents best practices and future directions, including federated learning for privacy preserving training, real time anomaly detection, comprehensive governance frameworks, and emerging research in quantum resistant cryptography and confidential computing. These approaches collectively form a robust framework for addressing the unique security and privacy challenges of AI deployment in financial services.