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Securing Generative AI

Securing Generative AI

Instructor: Pearson

1 module

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

1 module

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

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

Skills you'll gain

Details to know

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There is 1 module in this course

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.

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.

What's included

36 videos7 assignments

36 videosTotal 220 minutes

Introduction2 minutes
Learning objectives1 minute
Understanding the Significance of LLMs in the AI Landscape7 minutes
Exploring the Resources for this Course - GitHub Repositories and Others2 minutes
Introducing Retrieval Augmented Generation (RAG)12 minutes
Understanding the OWASP Top-10 Risks for LLMs5 minutes
Exploring the MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) Framework5 minutes
Understanding the NIST Taxonomy and Terminology of Attacks and Mitigations7 minutes
Learning objectives1 minute
Defining Prompt Injection Attacks11 minutes
Exploring Real-life Prompt Injection Attacks3 minutes
Using ChatML for OpenAI API Calls to Indicate to the LLM the Source of Prompt Input10 minutes
Enforcing Privilege Control on LLM Access to Backend Systems6 minutes
Best Practices Around API Tokens for Plugins, Data Access, and Function-level Permissions3 minutes
Understanding Insecure Output Handling Attacks3 minutes
Using the OWASP ASVS to Protect Against Insecure Output Handling4 minutes
Learning objectives0 minutes
Understanding Training Data Poisoning Attacks4 minutes
Exploring Model Denial of Service Attacks3 minutes
Understanding the Risks of the AI and ML Supply Chain8 minutes
Best Practices when Using Open-Source Models from Hugging Face and Other Sources12 minutes
Securing Amazon BedRock, SageMaker, Microsoft Azure AI Services, and Other Environments16 minutes
Learning objectives1 minute
Understanding Sensitive Information Disclosure2 minutes
Exploiting Insecure Plugin Design3 minutes
Avoiding Excessive Agency3 minutes
Learning objectives0 minutes
Understanding Overreliance5 minutes
Exploring Model Theft Attacks4 minutes
Understanding Red Teaming of AI Models13 minutes
Learning objectives1 minute
Understanding the RAG, LangChain, Llama Index, and AI Orchestration17 minutes
Securing Embedding Models9 minutes
Securing Vector Databases12 minutes
Monitoring and Incident Response7 minutes
Securing Generative AI: Summary1 minute

7 assignmentsTotal 210 minutes

Introduction to AI Threats and LLM Security Quiz30 minutes
Understanding Prompt Injection & Insecure Output Handling Quiz30 minutes
Training Data Poisoning, Model Denial of Service & Supply Chain Vulnerabilities Quiz30 minutes
Sensitive Information Disclosure, Insecure Plugin Design, and Excessive Agency Quiz30 minutes
Overreliance, Model Theft, and Red Teaming AI Models Quiz30 minutes
Protecting Retrieval Augmented Generation (RAG) Implementations Quiz30 minutes
End of Course Assessment 30 minutes

Instructor

Pearson

236 Courses6,035 learners

Offered by

Pearson

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Frequently asked questions

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.