A Trustworthy Generative AI Risk Assessment Framework for Enterprise Information Systems
Keywords:
generative AI, risk assessment, enterprise information systems, trustworthiness, governance, socio-technical systems, fairness, robustness, sustainability, regulatory complianceAbstract
The rapid integration of generative artificial intelligence into enterprise information systems presents unprecedented opportunities for automation, personalization, and knowledge synthesis, yet simultaneously introduces complex risks that challenge established governance and security paradigms. Existing risk assessment frameworks often fail to account for the unique properties of generative models, including emergent capabilities, opaque reasoning paths, and susceptibility to adversarial manipulation. This paper proposes a comprehensive risk assessment framework designed specifically for trustworthy generative AI within enterprise contexts, emphasizing systemic evaluation across technical, organizational, and regulatory dimensions. The framework integrates principles from socio-technical systems theory, software engineering, and responsible AI to address structural trade-offs between innovation and control, performance and interpretability, and autonomy and oversight. Architecture-level considerations such as model provenance, data lineage, and deployment topology are examined alongside governance mechanisms including continuous monitoring, audit trails, and incident response protocols. Special attention is given to fairness, robustness, and sustainability as cross-cutting concerns that must be embedded throughout the lifecycle of generative AI services. The framework further incorporates policy implications by mapping compliance requirements from emerging regulations onto concrete risk metrics and decision thresholds. Through detailed analytical discussion and illustrative case comparisons, the paper demonstrates how enterprises can systematically evaluate generative AI deployments without stifling beneficial use cases. The proposed approach moves beyond checklist-based assessment toward a dynamic, context-aware methodology that evolves with model updates and shifting operational environments. By aligning risk management with trustworthiness principles, the framework provides a foundation for enterprises to responsibly harness generative AI while maintaining accountability and resilience in their information systems.
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