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.
Securing Generative AI
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
您将获得的技能
- Security Controls
- AI Security
- Open Web Application Security Project (OWASP)
- MITRE ATT&CK Framework
- Threat Modeling
- Retrieval-Augmented Generation
- Application Security
- Security Testing
- Large Language Modeling
- Generative AI
- Secure Coding
- Threat Management
- Prompt Engineering
- AI Orchestration
- Embeddings
- AI Workflows
- Vector Databases
- LangChain
- Responsible AI
- 技能部分已折叠。显示 8 项技能，共 19 项。
要了解的详细信息
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7 项作业
September 2025
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.
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