Generative AI, Cloud Computing, and Configurable Workflow Architecture for Next-Generation Risk Management

Abstract

Risk management is dominated by text. Incident reports, control descriptions, audit findings, regulatory updates, and risk committee papers are written, read, compared, and summarized by people, often under time pressure. Generative artificial intelligence (AI), and large language models (LLMs) in particular, can draft, summarize, classify, and compare such text at scale. It also introduces new risks: fabricated content, sensitivity to manipulated inputs, and outputs that are hard to audit. This article argues that generative AI becomes safe and useful in risk management when it is placed inside configurable workflows rather than used as a free-standing assistant. Each generative step is configured with its task, its approved sources, the checks its output must pass, and the person who must approve it. The article proposes an architecture in which a configurable workflow engine calls a generative AI gateway, which grounds outputs in the organization’s own documents through retrieval, validates them, and records every input and output. All components run as cloud services with self-healing and safe fallback. Using a design-oriented approach grounded in eleven studies published between 2020 and 2023, the article maps the evidence base, places generative tasks across the risk lifecycle, sets out a controlled generative step, and maps generative AI risks to workflow controls. It illustrates the design with a pharmaceutical manufacturer managing quality deviations and regulatory change. It concludes that the workflow, not the model, should decide what generative AI is allowed to do.