on-demand course

AI Security and Responsible AI Practices

with Omar Santos / Dr. Petar Radanliev

March 2024

Intermediate

4h 55m

English

Pearson

Closed Captioning available in English

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Course outline

AI Security and Responsible AI Practices: Introduction
1m 58s
Module introduction
34s
Learning objectives
1m 5s
1.1 Delving into supervised, unsupervised, and reinforcement learning
7m 23s
1.2 Diving into applications and use cases
5m 29s
1.3 Strategies in preprocessing and feature engineering
7m 17s
1.4 Navigating through popular and traditional ML algorithms
13m 3s
1.5 Exploring model evaluation and validation
6m 56s
Learning objectives
1m 0s
2.1 Introduction to generative AI
2m 35s
2.2 Delving into large language models (LLMs)
8m 29s
2.3 Exploring examples of AI applications we use on a daily basis
8m 52s
2.4 Going beyond ChatGPT, MidJourney, LLaMA
10m 2s
2.5 Exploring Hugging Face, LangChain Hub, and other AI model and dataset sharing hubs
5m 53s
2.6 Modern AI model training environments
7m 28s
2.7 Introducing LangChain, templates, and agents
10m 12s
2.8 Fine tuning AI Models using LoRA and QLoRA
9m 24s
2.9 Introducing retrieval-augmented generation (RAG)
4m 3s
Module introduction
48s
Learning objectives
1m 2s
3.1 Importance of security in AI and ML systems
2m 46s
3.2 OWASP top 10 risks for LLM applications
3m 7s
3.3 Exploring prompt injection attacks
5m 14s
3.4 Surveying data poisoning attacks
4m 40s
3.5 Understanding insecure output handling
5m 6s
3.6 Discussing insecure plugin design
4m 12s
3.7 Understanding excessive agency
4m 7s
3.8 Exploring model theft attacks
2m 40s
3.9 Understanding overreliance of AI systems
4m 7s
Learning objectives
1m 43s
4.1 Exploring the MITRE ATLAS framework
4m 23s
4.2 AI supply chain security
6m 39s
4.3 Automated vulnerability discovery and creating exploits at scale
6m 32s
4.4 Intelligent data harvesting, OSINT, automating phishing, and social engineering attacks
9m 35s
4.5 Exploring examples of deepfakes and synthetic media
4m 14s
4.6 Dynamic obfuscation of attack vectors
3m 9s
Learning objectives
1m 31s
5.1 Secure development practices
3m 24s
5.2 Monitoring and auditing
12m 44s
5.3 Software Bill of Materials (SBOMs) and AI Bill of Materials (AI BOMs)
9m 15s
5.4 Using CSAF and VEX to accelerate vulnerability management
5m 21s
Module introduction
1m 2s
Learning objectives
1m 1s
6.1 Understanding key privacy considerations in AI implementations
1m 29s
6.2 Bias and fairness in AI and ML systems
5m 49s
6.3 Transparency and accountability
4m 42s
6.4 Understanding differential privacy
4m 55s
6.5 Exploring secure multi-party computation (SMPC)
4m 24s
6.6 Understanding homomorphic encryption
3m 9s
6.7 Understanding the AI data lifecycle management
5m 20s
6.8 Delving into federated learning
5m 57s

Content preview from AI Security and Responsible AI Practices

2.5 Exploring Hugging Face, LangChain Hub, and other AI model and dataset sharing hubs

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Beyond the Algorithm: AI, Security, Privacy, and Ethics

Omar Santos, Petar Radanliev

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Lester Nichols

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Alfredo Deza, Noah Gift

Publisher Resources

ISBN: 9780138361600

AI Security and Responsible AI Practices

with Omar Santos / Dr. Petar Radanliev

Introduction

Module 1: Fundamentals of AI and ML

Lesson 1: Overview of AI and ML Implementations

Lesson 2: Generative AI and Large Language Models (LLMs)

Module 2: AI and ML Security

Lesson 3: Fundamentals of AI and ML Security

Lesson 4: How Attackers Are Using AI to Perform Attacks

Lesson 5: AI System and Infrastructure Security

Module 3: Privacy and Ethical Considerations

Lesson 6: Privacy and AI Fundamentals

Lesson 7: AI Ethics

Lesson 8: Legal and Regulatory Compliance

Summary

Content preview from AI Security and Responsible AI Practices

2.5 Exploring Hugging Face, LangChain Hub, and other AI model and dataset sharing hubs

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

You might also like

Beyond the Algorithm: AI, Security, Privacy, and Ethics

Cybersecurity Architect's Handbook

Generative AI Security Conference

Responsible Generative AI and Local LLMs

Publisher Resources

Introduction

Module 1: Fundamentals of AI and ML

Lesson 1: Overview of AI and ML Implementations

Lesson 2: Generative AI and Large Language Models (LLMs)

Module 2: AI and ML Security

Lesson 3: Fundamentals of AI and ML Security

Lesson 4: How Attackers Are Using AI to Perform Attacks

Lesson 5: AI System and Infrastructure Security

Module 3: Privacy and Ethical Considerations

Lesson 6: Privacy and AI Fundamentals

Lesson 7: AI Ethics

Lesson 8: Legal and Regulatory Compliance

Summary

Content preview from AI Security and Responsible AI Practices

2.5 Exploring Hugging Face, LangChain Hub, and other AI model and dataset sharing hubs

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,and much more.

You might also like

Beyond the Algorithm: AI, Security, Privacy, and Ethics

Cybersecurity Architect's Handbook

Generative AI Security Conference

Responsible Generative AI and Local LLMs

Publisher Resources

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.