The rapid development of digital economy has led to the emergence of various black and shadow internet industries, which pose potential risks that can be identified and managed through digital risk management (DRM) that uses different techniques such as machine learning and deep learning. The evolution of DRM architecture has been driven by changes in data forms. However, the development of AI-generated content (AIGC) technology, such as ChatGPT and Stable Diffusion, has given black and shadow industries powerful tools to personalize data and generate realistic images and conversations for fraudulent activities. This poses a challenge for DRM systems to control risks from the source of data generation and to respond quickly to the fast-changing risk environment. This paper aims to provide a technical analysis of the challenges and opportunities of AIGC from upstream, midstream, and downstream paths of black/shadow industries and suggest future directions for improving existing risk control systems. The paper will explore the new black and shadow techniques triggered by generative AI technology and provide insights for building the next-generation DRM system.

翻译：数字经济的快速发展催生了各类网络黑灰产业，这些产业通过数字风险管理（DRM）技术（如机器学习和深度学习）可被识别与管理。DRM架构的演进始终受数据形态变化的驱动。然而，以ChatGPT和Stable Diffusion为代表的AI生成内容（AIGC）技术，为黑灰产业提供了个性化数据伪造、生成逼真图像与对话进行欺诈活动的强大工具。这对DRM系统提出了从数据生成源头控制风险、快速响应瞬息万变的风险环境的双重挑战。本文旨在从黑灰产业的上游、中游、下游路径出发，对AIGC带来的技术挑战与机遇进行系统分析，并提出改进现有风控系统的未来方向。本文将探讨生成式AI催生的新型黑灰技术，为构建下一代DRM系统提供洞见。