Preventing Cross-Tenant Data Exposure in Financial Platforms Through AI-Assisted Security and Enterprise Architecture Controls

Abstract

Modern financial platforms are increasingly offering banking, payments, wealth management, and other financial services through shared multi-tenant architectures. However, with the sharing of data among tenants, significant security concerns arise that may not be completely addressed by traditional controls. This paper introduces a framework for the enterprise architecture which combines tenant isolation, identity management, API gateway enforcement, secure identifiers, immutable audit logs, and AI-driven risk detection. Unlike previous approaches, which mainly focused on AI model creation, this framework views AI as an assistive overlay that enhances rather than substitutes for deterministic architectural controls. We have enumerated nine cross-tenant exposure vectors, outlined a layered architecture with eight levels of control, and demonstrated, using scenario-based metrics, the added security value in the use of AI-driven anomaly detection and policy violation analysis. The paper also provides implementation guidance, data privacy considerations, and adoption planning for financial platform architectures. The proposed framework brings together enterprise security architecture and AI-driven risk intelligence to enable financial systems to be resilient across multiple tenants.