SecureBank: A Financially-Aware Zero Trust Architecture for High-Assurance Banking Systems

TL;DR

The paper tackles the rising attack surface in modern financial ecosystems by arguing that perimeter-based security is insufficient for cloud-native, API-driven banking. It introduces SecureBank, a financially aware Zero Trust architecture that integrates Financial Zero Trust, Adaptive Identity Scoring, Contextual Micro-Segmentation, and Impact-Driven Automation, underpinned by a formal trust-risk model and new metrics such as the Financial Threat Score ($FTS$), Transactional Integrity Index ($TII$), Identity Trust Adaptation Level ($ITAL$), and Security Automation Efficiency ($SAE$). A Monte Carlo simulation demonstrates that SecureBank significantly improves automated attack handling and accelerates identity trust adaptation while maintaining regulator-aligned transactional integrity. The work provides a reproducible reference framework for evaluating financially aware Zero Trust in regulated financial environments and offers concrete metrics and segmentation logic designed to bridge security with financial risk management.

Abstract

Financial institutions increasingly rely on distributed architectures, open banking APIs, cloud native infrastructures, and high frequency digital transactions. These transformations expand the attack surface and expose limitations in traditional perimeter based security models. While Zero Trust architectures provide essential security principles, most existing frameworks do not explicitly incorporate transactional semantics, financial risk modeling, adaptive identity trust, or automation weighted by economic impact. This paper introduces SecureBank, a financially aware and context adaptive Zero Trust architecture designed specifically for high assurance banking systems. The proposed framework integrates Financial Zero Trust, Adaptive Identity Scoring, Contextual Micro Segmentation, and Impact Driven Security Automation. A Monte Carlo simulation evaluates SecureBank against a representative rule based baseline architecture using metrics such as the Transactional Integrity Index (TII), Identity Trust Adaptation Level (ITAL), and Security Automation Efficiency (SAE). The results demonstrate that SecureBank significantly improves automated attack handling and accelerates identity trust adaptation while preserving conservative and regulator aligned levels of transactional integrity. Beyond experimental validation, SecureBank is intended to serve as a reference architecture and evaluation baseline for financially aware Zero Trust systems in regulated financial environments.

Figures (9)

• Figure 1: SecureBank™ Architecture Overview: multi-layered security framework integrating Financial Zero Trust, Adaptive Identity Scoring, Contextual Micro-Segmentation, and Impact-Driven Automation components with real-time threat intelligence and regulatory compliance modules.
• Figure 2: SecureBank™ Operational Workflow: end-to-end transaction processing flow showing continuous trust evaluation, dynamic policy enforcement, and automated response mechanisms across all architectural layers.
• Figure 3: Contextual Micro-Segmentation Map: business-aligned network segmentation showing isolated zones for payments, settlements, risk analytics, AML operations, and customer services with controlled inter-zone communication policies.
• Figure 4: Threat Model and Attack Surface Analysis: comprehensive threat landscape covering credential compromise, API exploitation, lateral movement, data exfiltration, and insider threats specific to banking environments.
• Figure 5: Financial Threat Score (FTS) Computation Model: multi-factor threat scoring algorithm integrating threat intelligence, transaction risk assessment, and contextual anomaly detection with weighted parameter contributions ($\alpha=0.5$, $\beta=0.3$, $\gamma=0.2$).