courseraIT и технологии

Ethical Hacking: Securing Generative AI

Курс от Pearson

Средний≈ 7.2 чАнглийский

О курсе

This course is designed to equip you with the knowledge to protect large language models (LLMs) and AI systems from emerging threats. You will explore critical security challenges such as prompt injection, training data poisoning, and model theft. You will gain insights into frameworks like MITRE ATLAS and NIST, and learn to implement best practices for securing AI ecosystems. By the end of this course, you will be proficient in identifying vulnerabilities, applying mitigation strategies, and enhancing the resilience of AI systems.

Навыки, которые вы освоите

AI Security Large Language Modeling Vector Databases Prompt Engineering LangChain Generative AI Embeddings LangGraph Continuous Monitoring Threat Modeling Event Monitoring Security Testing Threat Detection MITRE ATT&CK Framework Cyber Threat Intelligence LLM Application Threat Management

Программа курса

1 модулей · 43 учебных материалов

01Securing Generative AI43 материалов

Module Introduction

Module IntroductionВидео

Introduction to AI Threats and LLM Security

Learning objectivesВидео Understanding the Significance of LLMs in the AI LandscapeВидео Exploring the Resources for this Course - GitHub Repositories and OthersВидео

Учитесь у экспертов

Pearson

Преподаватель курса

Nick Garner

Solution Architect

Ethical Hacking: Securing Generative AI

О курсе

Навыки, которые вы освоите

Программа курса

Module Introduction

Introduction to AI Threats and LLM Security

Учитесь у экспертов

Pearson

Nick Garner

Understanding Prompt Injection & Insecure Output Handling

Training Data Poisoning, Model Denial of Service & Supply Chain Vulnerabilities

Sensitive Information Disclosure, Insecure Plugin Design, and Excessive Agency

Overreliance, Model Theft, and Red Teaming AI Models

Protecting Retrieval Augmented Generation (RAG) Implementations

Certified Ethical Hacker (CEH): Summary