中This course offers a comprehensive exploration into the crucial security measures necessary for the deployment and development of various AI implementations, including large language models (LLMs) and Retrieval-Augmented Generation (RAG). It addresses critical considerations and mitigations to reduce the overall risk in organizational AI system development processes. Experienced author and trainer Omar Santos emphasizes “secure by design” principles, focusing on security outcomes, radical transparency, and building organizational structures that prioritize security. You will be introduced to AI threats, LLM security, prompt injection, insecure output handling, and Red Team AI models. The course concludes by teaching you how to protect RAG implementations. You learn about orchestration libraries such as LangChain, LlamaIndex, and others, as well as securing vector databases, selecting embedding models, and more.
您将学到什么
Explore security for deploying and developing AI applications, RAG, agents, and other AI implementations
Learn hands-on with practical skills of real-life AI and machine learning cases
Incorporate security at every stage of AI development, deployment, and operation
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September 2025
7 项作业
了解顶级公司的员工如何掌握热门技能
该课程共有1个模块
This module provides a comprehensive overview of generative AI security, covering threats and mitigation strategies for large language models and related systems. Topics include prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, sensitive information disclosure, insecure plugin design, excessive agency, overreliance, model theft, red teaming, and securing Retrieval Augmented Generation (RAG) implementations. Learners gain practical knowledge of industry frameworks, best practices, and tools to safeguard AI technologies in production environments.
涵盖的内容
36个视频7个作业
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