AI Product Security: Foundations and Proactive Security for AI
1h 43mIntermediate2025-01-28
Authors
Reet Kaur
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.
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.
Skills covered
Software Development SecurityArtificial Intelligence FoundationsArtificial Intelligence (AI)CybersecurityOne-Off
Concepts
Introduction
- Securing AI products
- Why does AI security matter
Fundamentals of AI Security
- Essentials of AI security
- Common threats and vulnerabilities in AI systems
- Ethical concerns, privacy, fairness, and user rights
- Security across the AI life cycle
Building Resilient AI - Securing AI Models, Data, and Deployment
- Overview of adversarial AI attacks
- Attacks on AI algorithms with real-world examples
- Attacks on filters
- Subversion of AI artifacts in supply chain attacks
- Defending against adversarial attacks
- Data security in AI systems
- Model security - Protecting AI models
- Securing AI deployment pipelines
- Secure deployment strategies for AI systems
AI Security Governance, Risk Management, and Compliance
- Governance in AI product security
- AI risk management
- AI audit and compliance
System Design Principles
- Foundational principles of AI system design
- Advanced principles of AI system design
Conclusion
- Next steps