RESILIENT OBSERVABILITY ARCHITECTURES FOR VOLATILITY-DRIVEN FINANCIAL SYSTEMS: INTEGRATING RELIABILITY ENGINEERING, SRE, AND AI-ENHANCED MONITORING

Abstract

Modern financial systems increasingly operate under conditions of persistent volatility driven by algorithmic trading, globalized capital flows, geopolitical instability, and highly interconnected digital infrastructures. In this environment, even minor technical disruptions can cascade into systemic financial risk, transforming localized outages into market-wide disturbances. Consequently, resilience engineering, site reliability engineering, and observability have emerged as foundational paradigms for safeguarding financial digital infrastructures. However, the theoretical and operational integration of these paradigms remains insufficiently explored in scholarly literature. This article develops a comprehensive research-driven framework that unifies resilience engineering, AI-enhanced observability, and site reliability engineering to ensure continuous uptime and stability in financial systems operating under extreme volatility.

Drawing upon contemporary research in reliability engineering, machine-learning-based monitoring, and distributed systems observability, this study synthesizes insights from financial resilience theory and cloud-native operational practices. Central to this synthesis is the recognition that financial systems are not merely computational platforms but socio-technical ecosystems in which technical failures are inseparable from market behaviors, regulatory responses, and investor psychology. As demonstrated by Dasari (2025), volatility amplifies the fragility of financial platforms by compressing decision cycles and magnifying the consequences of service disruptions. The present study builds upon this insight by situating observability and reliability engineering as instruments of institutional resilience rather than purely technical safeguards.

Methodologically, the article employs an integrative literature-driven analytical design that reconstructs conceptual models from engineering, finance, and cloud operations into a unified resilience framework. Rather than presenting empirical datasets, the study systematically analyzes and cross-interprets existing scholarly evidence, thereby revealing patterns, contradictions, and theoretical gaps. This approach enables the development of a conceptual architecture for financial system observability that incorporates predictive analytics, distributed tracing, and causal inference into a coherent resilience strategy.

The results demonstrate that financial uptime during volatility is best understood as an emergent property of tightly coupled observability and reliability mechanisms. AI-driven anomaly detection, when embedded within full-stack observability frameworks, enables early identification of market-relevant system degradation. At the same time, site reliability engineering provides the organizational scaffolding through which such intelligence is translated into action. The discussion advances these findings by critically comparing competing scholarly perspectives on resilience, highlighting both the technological and institutional constraints that shape real-world implementation.

By bridging theoretical domains that are often treated separately, this article contributes a novel interdisciplinary foundation for designing and governing financial cloud infrastructures. It concludes that resilient financial observability is not merely a technical objective but a strategic imperative for maintaining market trust, regulatory compliance, and systemic stability in an era of perpetual digital volatility.