SecGenAI：增强澳大利亚国家利益关键技术中基于云的生成式 AI 应用的安全性
SecGenAI: Enhancing Security of Cloud-based Generative AI Applications within Australian Critical Technologies of National Interest
AI总结：
本文提出 SecGenAI 安全框架，面向云端 GenAI 与 RAG 应用，整合端到端分析及澳大利亚隐私、伦理和网络安全要求，以增强合规性、可靠性与可信度。
摘要：
生成式 AI（GenAI）技术的快速发展为澳大利亚国家利益关键技术带来了变革性机遇，同时也引入了独特的安全挑战。本文提出 SecGenAI，这是一个面向基于云的 GenAI 应用的综合安全框架，重点关注检索增强生成（RAG）系统。SecGenAI 处理功能、基础设施和治理需求，整合端到端安全分析，以生成强调数据隐私、安全部署和共同责任模型的规范。SecGenAI 与澳大利亚隐私原则、AI 伦理原则以及澳大利亚网络安全中心和数字转型局的指南保持一致，可缓解数据泄露、对抗性攻击和模型反演等威胁。该框架的新颖方法将先进机器学习技术与稳健安全措施相结合，在确保符合澳大利亚法规的同时，提高 GenAI 系统的可靠性和可信度。本研究通过为产业界安全实施 GenAI 提供可执行策略、促进 AI 应用创新并维护国家利益，为智能系统领域作出贡献。
英文摘要：
The rapid advancement of Generative AI (GenAI) technologies offers transformative opportunities within Australia's critical technologies of national interest while introducing unique security challenges. This paper presents SecGenAI, a comprehensive security framework for cloud-based GenAI applications, with a focus on Retrieval-Augmented Generation (RAG) systems. SecGenAI addresses functional, infrastructure, and governance requirements, integrating end-to-end security analysis to generate specifications emphasizing data privacy, secure deployment, and shared responsibility models. Aligned with Australian Privacy Principles, AI Ethics Principles, and guidelines from the Australian Cyber Security Centre and Digital Transformation Agency, SecGenAI mitigates threats such as data leakage, adversarial attacks, and model inversion. The framework's novel approach combines advanced machine learning techniques with robust security measures, ensuring compliance with Australian regulations while enhancing the reliability and trustworthiness of GenAI systems. This research contributes to the field of intelligent systems by providing actionable strategies for secure GenAI implementation in industry, fostering innovation in AI applications, and safeguarding national interests.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
