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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ai-risk-security-secure-coding/
课程评论:没有评论
课程名称:AI风险与安全 - 安全编码 概述:该课程旨在弥补人工智能与安全软件开发之间的差距,为学习者提供利用AI驱动的代码生成工具的技能,同时优先考虑安全性和最佳实践。完成课程后,开发人员、AI爱好者、风险管理人员和安全专业人士将准备好在不断发展的AI辅助软件开发领域中引领潮流。 参与者将深入研究AI语言模型的架构,了解其内部工作原理以及如何有效应用于软件开发。课程以AI在代码生成中的介绍为开端,涵盖AI编码的历史与演变,并介绍当前的AI工具和技术,如GitHub Copilot和GPT-4。学习者通过实践练习和案例研究,体验AI生成的安全与不安全代码的对比。 课程接下来将讨论AI代码生成的优势与风险,强调AI如何提高开发速度和效率,同时也存在如偏见和训练数据中的过时实践等潜在风险。参与者将学习如何通过全面评估和伦理考量来降低这些风险。 课程特别讲解了到2025年AI代码生成的十大风险,引导学习者实行相应控制以避免这些风险。讲座探讨了AI生成代码可能带来的各种问题,如偏见、法律违规、过时实践和安全疏漏,强调了保持警惕的重要性。课程中还涉及安全问题、隐私泄露、算法逻辑错误以及过时API所带来的风险。 随后两段讲座展示了将自然语言转换为安全AI生成代码的过程。参与者将学习安全编码的关键原则,以及如何巧妙编写提示来指导AI模型生成安全代码。在讲座中具体演示了如何通过有效的提示,确保生成的代码具备安全特性。 在第六讲中,参与者将了解如何通过具体的评估指标来评估AI生成代码的可靠性、安全性和质量。讨论了重要的代码可靠性指标,如平均故障时间(MTTF)、平均修复时间(MTTR)和环形复杂度。同时强调了识别安全漏洞、保持一致性能与增强对AI工具信任的重要性。 最后一讲专注于如何将之前讲座中的评估指标应用于实际场景。通过演示展示如何确保AI生成代码不仅具备功能性,还具备安全性和可维护性。强调开发者在使用AI生成代码时要“可信但需验证”,须通过自动化与手动技术确认其符合安全和性能预期。 通过深入的真实案例研究和专家见解,学习者将获得实际知识,以自信地在编码项目中利用AI,确保最高水平的安全性和可靠性。该课程赋予学习者迎接未来挑战的能力,使其能够适应新兴的AI发展并在项目中实施强有力的安全措施,成为任何希望在该领域取得成功的人的宝贵资源。今天就报名,转变您对安全和创新软件开发的看法。
This course bridges the gap between artificial intelligence and secure software development, equipping learners with the skills to harness AI-driven code generation tools while prioritizing security and best practices. By the end of the course, developers, AI enthusiasts, risk managers, and security professionals will be well-prepared to lead the charge in the evolving landscape of AI-assisted software development.Participants will delve into the architecture of AI language models, understanding their inner workings and how they can be effectively utilized in software development. The course starts with an introduction to AI in code generation, covering the history and evolution of AI in coding, and presenting current AI tools and technologies like GitHub Copilot and GPT-4. Learners will get hands-on experience through practical exercises and case studies, contrasting secure and insecure code generated by AI.The curriculum then addresses the benefits and risks of AI code generation, highlighting how AI can increase development speed and efficiency while also presenting potential risks such as biases and deprecated practices in training data. Participants will learn how to mitigate these risks through thorough evaluation and ethical considerations.A dedicated lecture on the top 10 risks for 2025 when it comes to AI code generation guides learners on what controls they need to implement to avoid these risks. This lecture explores the potential pitfalls associated with AI-generated code, such as biases, legal violations, deprecated practices, and security oversights. AI can accelerate development, but it also introduces challenges like algorithmic bias and unintentional inclusion of GPL-licensed code, potentially forcing projects into open-source. Examples, such as recruiting tools discriminating against women and commercial products using GPL-licensed code without proper compliance, highlight the importance of vigilance. The lecture also covers security issues, privacy leaks, logic errors in algorithms, and risks from deprecated APIs, mentioning common breaches. These real-world examples reinforce the need for proper controls and oversight when integrating AI into development workflows.The next two lectures demonstrate the process of transforming human language into secure AI-generated code. Participants learn key secure coding principles and how to craft effective prompts to guide AI models in producing secure code. The demonstration emphasizes prompt engineering, showing the difference between a simple prompt ("Generate a React login form") and a secure one ("Generate a React login form with input validation, CSRF protection, and secure handling against XSS"). Additionally, the lecture discusses secure coding practices in React, such as protecting against XSS attacks and ensuring client-side authentication workflows are robustIn lecture 6, participants will learn how to assess the reliability, security, and quality of AI-generated code using specific evaluation metrics. Key code reliability indicators such as Mean Time to Failure (MTTF), Mean Time to Repair (MTTR), and cyclomatic complexity are discussed. The importance of identifying security gaps, maintaining consistent performance, and fostering trust in AI tools are emphasized.Finally, the last lecture focuses on integrating the evaluation metrics from previous lectures into real-world scenarios. Demos are presented to showcase how these metrics can be applied to ensure that AI-generated code is not only functional but secure and maintainable. The lecture reinforces the idea that developers must "trust but verify" when it comes to AI-generated code, using both automated and manual techniques to confirm that the code meets security and performance expectationsThrough in-depth real-world case studies and expert insights, learners will gain practical knowledge to confidently leverage AI in their coding projects, ensuring the highest standards of security and reliability. This comprehensive course empowers learners to stay ahead of the curve, adapt to new AI advancements, and implement robust security measures in their projects, making it a valuable resource for anyone looking to excel in the field. Enrol today to transform your approach to secure and innovative software development.