AI Product Security: Foundations and Proactive Security for AI

Course details

This course provides a comprehensive foundation in AI product security, focusing on the unique challenges and risks associated with securing AI systems. Explore the AI threat landscape, from adversarial attacks to data poisoning, and gain practical skills in implementing proactive security measures throughout the AI product lifecycle. The course covers secure design principles, data security, and model protection, alongside strategies for continuous security monitoring and governance. By the end of the course, you will be equipped to build and maintain robust AI security frameworks for real-world applications, ensuring the integrity and safety of AI-driven products.

Learning objectives
Identify the key security threats specific to AI products and differentiate them from traditional software security risks.
Apply secure AI development practices, including data security, model protection, and secure deployment techniques.
Implement proactive security strategies such as adversarial defense mechanisms and AI risk management within AI products.
Design a comprehensive security strategy for an AI product, incorporating governance, ethical considerations, and emerging threat mitigations.

Concepts

0. Introduction

01 - Securing AI products
02 - Why does AI security matter

1. Fundamentals of AI Security

03 - Essentials of AI security
04 - Common threats and vulnerabilities in AI systems
05 - Ethical concerns, privacy, fairness, and user rights
06 - Security across the AI life cycle

2. Building Resilient AI - Securing AI Models, Data, and Deployment

07 - Overview of adversarial AI attacks
08 - Attacks on AI algorithms with real-world examples
09 - Attacks on filters
10 - Subversion of AI artifacts in supply chain attacks
11 - Defending against adversarial attacks
12 - Data security in AI systems
13 - Model security - Protecting AI models
14 - Securing AI deployment pipelines
15 - Secure deployment strategies for AI systems

3. AI Security Governance, Risk Management, and Compliance

16 - Governance in AI product security
17 - AI risk management
18 - AI audit and compliance

4. System Design Principles

19 - Foundational principles of AI system design
20 - Advanced principles of AI system design

Conclusion

21 - Next steps

AI Product Security: Foundations and Proactive Security for AI

Authors

Reet Kaur