Abstract : The emergence of Generative Artificial Intelligence (AI) systems has expanded the boundaries of traditional model development and validation, introducing new dimensions of model risk. Existing Model Risk Management (MRM) standards such as SR 11-7 and SS1/23 remain foundational; however, their application must evolve to address the dynamic and context-dependent behaviour of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) architectures, and multi-agent environments. These systems pose novel and high-impact risks due to their generative nature, contextual variability, and ability to self-orchestrate actions. Ensuring that such models produce consistent, auditable, and risk-mitigated outcomes has become critical, yet validators face growing challenges in assessing conceptual soundness, monitoring reasoning reliability, and evaluating control effectiveness within existing MRM frameworks. This paper proposes GEN-5, a five-pillar validation and assurance framework that adapts established Model Risk Management (MRM) principles to the unique behaviours and risks of Generative AI, RAG pipelines, and multi-component AI systems. GEN-5 provides a standardized template and actionable methodology for assessing conceptual soundness, performance accuracy, outcome reliability, control effectiveness, and continuous monitoring across AI-driven environments. It integrates both qualitative and quantitative evaluation techniques—including hallucination detection, prompt robustness, retrieval fidelity, semantic consistency, and reasoning stability—while emphasizing the essential role of governance, safety guardrails, and control assurance. By extending traditional MRM rigor to modern AI architectures, GEN-5 offers practitioners a policy-aligned, technically grounded approach for identifying, evaluating, and mitigating the novel risks introduced by Generative and enterprise-scale AI use cases.
Sinha Dr. Nabanita (Mon,) studied this question.