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LinkedIn Learning

AI Product Security: Foundations and Proactive Security for AI

via LinkedIn Learning

Overview

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Gain the knowledge and strategies to secure AI products from development to deployment, addressing unique threats and implementing proactive security measures.

Syllabus

Introduction
  • Securing AI products
  • Why does AI security matter?
1. 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
2. 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
3. AI Security Governance, Risk Management, and Compliance
  • Governance in AI product security
  • AI risk management
  • AI audit and compliance
4. System Design Principles
  • Foundational principles of AI system design
  • Advanced principles of AI system design
Conclusion
  • Next steps

Taught by

Reet Kaur

Reviews

4.5 rating at LinkedIn Learning based on 26 ratings

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