Cross-Domain Risk Aggregation: Synthesizing Cybersecurity, Ethics, and Compliance Frameworks for AI Audit Harmonization

AI risk assessment frameworks often operate in discipline-specific silos—cybersecurity, policy, ethics, operational risk, etc.—resulting in fragmented governance (Abisoye & Akerele, 2021; Schmitz et al., 2024). This project would develop and empirically test a cross-domain risk aggregation methodology, drawing from established frameworks in cybersecurity (like NIST), ethical risk assessment, and sectoral compliance (healthcare, finance, public policy). By mapping trade-offs and synergies between these domains, the framework would help auditors and policymakers harmonize standards, resolve conflicts (e.g., privacy vs. explainability), and address sector-specific vulnerabilities. The novelty lies in operationalizing “risk aggregation” (as discussed by Schmitz et al., 2024) in a way that is both theoretically grounded and practically actionable—enabling truly multidisciplinary, context-sensitive audits that keep pace with the complexity of modern AI systems.

References:

1. A High-Impact Data-Driven Decision-Making Model for Integrating Cutting-Edge Cybersecurity Strategies into Public Policy, Governance, and Organizational Frameworks. Ajayi Abisoye, Joshua Idowu Akerele (2021). International Journal of Multidisciplinary Research and Growth Evaluation.
2. A global scale comparison of risk aggregation in AI assessment frameworks. Anna Schmitz, Michael Mock, Rebekka Görge, Armin B. Cremers, Maximilian Poretschkin (2024). AI and Ethics.