Erick Sewando Mkuchu, Mary Magdalene Yeboah, Solomon Doe Adjaottor
This review discusses cyber resilience in the context of U.S. repurchase agreement (repo) and short-term funding markets set against a backdrop of liquidity stress scenarios affecting their capital adequacy implications amid an evolving digital financial landscape. The research examines the potential of cyber threats directed at several payment systems, FMIs, and settlement networks to disrupt flows of liquidity and how that can drive systemic risk and undermine the credibility of existing regulatory frameworks. It looks at operational resilience, the use of stress-testing methodologies and prudential standards to enhance financial institutions' capacity to absorb shocks stemming from cyber events. The review underscores the desirability of comprehensive approaches that integrate cybersecurity, liquidity risk management and capital planning for stability in wholesale funding markets.
Cyber resilience, Repo markets, Liquidity stress, Capital adequacy, Operational risk.
Oluwafemi Oluwagboyega Fabiyi, Mary Magdalene Yeboah
Artificial intelligence (AI)-enabled cloud systems increasingly support critical digital infrastructure in finance, healthcare, government, telecommunications, energy, transportation, and large-scale digital services. Their growing interdependence creates failure modes that can propagate across infrastructure, platform, application, model, cybersecurity, and governance boundaries. Existing resilience approaches address important parts of this problem - including cloud reliability, cyber resilience, AI risk management, operational continuity, and regulatory compliance - but generally treat them as separate domains. This fragmentation limits the ability of organizations to anticipate cross-layer disruptions, coordinate preventive and recovery controls, and convert operational experience into continuously improved resilience. This study develops the Unified Adaptive Cloud Resilience Framework (UACRF), a multi-layer, closed-loop framework for governing resilience in AI-cloud systems that support critical digital infrastructure. UACRF integrates four interdependent layers - infrastructure; platform and operations; application and AI lifecycle; and governance and compliance - with an eight-stage resilience lifecycle: Observe, Detect, Predict, Prevent, Withstand, Recover, Learn, and Adapt. The framework is grounded in continuous observability, predictive intelligence, cross-layer coordination, governed autonomy, closed-loop learning, and measurable resilience. It further specifies a cross-layer risk-propagation model, core constructs, and theoretical propositions that explain how integrated resilience capabilities can reduce detection delay, constrain failure propagation, improve recovery effectiveness, and strengthen long-term adaptive capacity. The paper contributes a common conceptual and operational language for connecting engineering controls, AI lifecycle governance, cybersecurity, and organizational decision-making. A cloud-agnostic reference architecture and an illustrative Amazon Web Services (AWS) mapping demonstrate how the theory can inform implementation without making the framework dependent on a single technology provider. UACRF therefore advances resilience from a collection of isolated controls toward a governed, measurable, and adaptive system capability. The proposed constructs and propositions provide a foundation for subsequent prototype development, fault-injection experiments, comparative evaluation, and sector-specific validation.
UACRF, AI-cloud resilience, critical digital infrastructure, adaptive governance, systemic risk…
