Abstract

Background: Contemporary organizations face unprecedented levels of uncertainty driven by technological disruption, climate change, geopolitical instability, and systemic interconnectedness. Traditional risk governance frameworks, often reliant on historical data and linear projections, struggle to address the complexity and ambiguity inherent in these high-uncertainty environments. The emergence of data-driven technologies—including machine learning, big data analytics, and artificial intelligence—offers new capabilities for risk assessment, decision support, and organizational resilience.

Objective: This scoping review systematically maps the landscape of data-driven risk governance in high-uncertainty environments, examining how organizations leverage advanced analytics and computational methods to enhance decision-making, manage complex risks, and build adaptive capacity. We identify key themes, methodological approaches, application domains, research gaps, and policy implications.

Methods: Following established scoping review methodology, we conducted a comprehensive literature search across multiple scholarly databases (SciSpace, Google Scholar, ArXiv) yielding 224 unique papers after deduplication. We analyzed the top 30 most relevant papers based on citation count and relevance scoring, extracting data on research focus, data-driven methods, and governance insights. Thematic synthesis identified five major themes: (1) machine learning and predictive analytics for risk assessment, (2) decision support systems under uncertainty, (3) organizational resilience and adaptive governance, (4) regulatory frameworks and policy considerations, and (5) sector-specific applications.

Results: The literature reveals a growing integration of machine learning techniques (neural networks, fuzzy logic, ensemble methods) with traditional risk management frameworks. Key applications span financial services, natural disaster management, supply chain resilience, healthcare, and critical infrastructure. Findings indicate that data-driven approaches enhance risk identification, enable real-time monitoring, and support scenario analysis under deep uncertainty. However, significant challenges persist regarding data quality, algorithmic transparency, governance structures for AI-enabled systems, and the integration of human judgment with automated decision-making.

Conclusions: Data-driven risk governance represents a paradigm shift from reactive, compliance-based approaches to proactive, adaptive systems capable of navigating high-uncertainty environments. Future research must address the governance of algorithmic decision-making, develop frameworks for responsible AI in risk management, bridge the gap between technical capabilities and organizational implementation, and establish standards for transparency and accountability. Policy implications include the need for regulatory frameworks that balance innovation with risk mitigation, capacity building for data literacy in governance roles, and cross-sector collaboration to develop best practices.

Keywords: risk governance, data-driven decision-making, machine learning, uncertainty management, organizational resilience, artificial intelligence, big data analytics, adaptive governance, risk assessment, decision support systems