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所在平台: Udemy |
课程主页: https://www.udemy.com/course/generative-ai-security-for-safe-use-in-organizations/
课程评论：没有评论
课程名称：组织中安全使用的生成性人工智能安全 课程概述：本课程全面介绍生成性人工智能（Generative AI, GenAI）技术及其在各行业中的应用，同时探讨相关的安全和伦理考量。参与者将深入了解生成性人工智能模型的类型、潜在漏洞及风险缓解的实用策略。通过详细的讲座、真实世界示例和案例研究，本课程旨在为专业人士提供必要的知识和工具，以促进生成性人工智能的安全和伦理使用。 在部署生成性人工智能时，安全是一个主要关注点。本课程将深入探讨特定威胁，包括数据中毒、提示注入攻击、模型逆转、数据泄露和模型盗窃等。通过来自不同产业的实例，参与者将学习识别和缓解这些风险。课程还将涉及伦理问题，包括人工智能生成内容中的偏见，为伦理的人工智能使用提供指导。 参与者将探索现实世界的案例研究，以理解这些风险的实际影响，并学习开发适合其组织需求的全面安全框架。课程强调生成性人工智能安全使用的最佳实践，包括防止恶意代码执行的沙盒化（sandboxing）重要性以及在组织内推广安全意识文化。 课程结束时，参与者将掌握确保生成性人工智能技术安全和伦理部署所需的知识和工具。 涵盖的主要内容包括： - 不同产业中的生成性人工智能应用 - 与生成性人工智能相关的安全风险 - 伦理问题和人工智能生成内容中的偏见 - 数据中毒和提示注入攻击 - 不同产业中的数据中毒示例 - 不同产业中的提示注入攻击示例 - 模型逆转和数据泄露 - 不同产业中的模型逆转示例 - 不同产业中的数据泄露示例 - 模型盗窃及其影响 - 不同产业中的模型盗窃示例 - 生成性人工智能中的幻觉现象 - 不足的沙盒化和恶意代码执行 - 不同产业中的不足沙盒化示例 - 不同产业中的恶意代码示例 - 公共人工智能所带来的主要威胁 - 案例研究 - 创建六步安全框架 - 生成性人工智能安全使用的最佳实践 - 在人工智能中促进安全意识文化 学习成果：课程结束时，参与者将能够： - 理解生成性人工智能模型的基本概念和类型。 - 识别生成性人工智能在各行业的多样化应用。 - 识别与生成性人工智能相关的安全风险和伦理问题。 - 实施减少安全威胁和促进伦理人工智能使用的最佳实践。 - 开发适合其组织需求的全面安全框架。 - 在人工智能开发和部署中培养安全意识和责任文化。
This course provides a comprehensive understanding of Generative AI (GenAI) technologies, their applications across various industries, and the associated security and ethical considerations. Participants will gain insights into the types of Generative AI models, their potential vulnerabilities, and practical strategies for mitigating risks. Through detailed lectures, real-world examples, and case studies, this course aims to equip professionals with the knowledge and tools necessary to promote secure and ethical use of Generative AI.Security is a major concern in the deployment of Generative AI, and this course will delve into specific threats such as data poisoning, prompt injection attacks, model inversion, data leakage, and model stealing. Through detailed examples from different industries, participants will learn to identify and mitigate these risks. Ethical concerns, including bias in AI-generated content, will also be addressed, providing guidelines for ethical AI use.Participants will explore real-world case studies to understand the practical implications of these risks and learn to develop a comprehensive security framework tailored to their organizational needs. The course will emphasize best practices for the safe use of Generative AI, including the importance of sandboxing to prevent malicious code execution and promoting a culture of security awareness within organizations.By the end of the course, participants will be equipped with the knowledge and tools necessary to ensure the secure and ethical deployment of Generative AI technologies. Key Areas Covered Are:Applications of Generative Al in various industriesSecurity Risks Associated with Generative AlEthical Concerns and Bias in Al-Generated ContentData Poisoning and Prompt Injection AttacksExample of Data Poisoning in different IndustriesExample of Prompt Injection Attacks in diferent IndustriesModel Inversion and Data LeakageExample of Model Inversion in different IndustriesExample of Data Leakage in different IndustriesModel Stealing and Its ImplicationsExample of Model Stealing in different IndustriesHallucinations in Generative AlInadequate Sandboxing and Malicious Code ExecutionExample of Inadequate Sandboxing in different IndustriesExample of Malicious Code in different IndustriesTop Threats Presented by Public Alcase studiescreating six steps security framework Best Practices for Safe Use of Generative Al Promoting a Culture of Security Awareness in AlLearning Outcomes:By the end of this course, participants will:Understand the fundamental concepts and types of Generative AI models.Recognize the diverse applications of Generative AI across various industries.Identify security risks and ethical concerns associated with Generative AI.Implement best practices for mitigating security threats and promoting ethical AI use.Develop a comprehensive security framework tailored to their organizational needs.Foster a culture of security awareness and responsibility in AI development and deployment.
