Course Outline

Introduction to AI Security Challenges

  • Understanding security risks unique to AI systems
  • Comparing traditional cybersecurity vs. AI cybersecurity
  • Overview of attack surfaces in AI models

Adversarial Machine Learning

  • Types of adversarial attacks: evasion, poisoning, and extraction
  • Implementing adversarial defenses and countermeasures
  • Case studies on adversarial attacks in different industries

Model Hardening Techniques

  • Introduction to model robustness and hardening
  • Techniques for reducing model vulnerability to attacks
  • Hands-on with defensive distillation and other hardening methods

Data Security in Machine Learning

  • Securing data pipelines for training and inference
  • Preventing data leakage and model inversion attacks
  • Best practices for managing sensitive data in AI systems

AI Security Compliance and Regulatory Requirements

  • Understanding regulations around AI and data security
  • Compliance with GDPR, CCPA, and other data protection laws
  • Developing secure and compliant AI models

Monitoring and Maintaining AI System Security

  • Implementing continuous monitoring for AI systems
  • Logging and auditing for security in machine learning
  • Responding to AI security incidents and breaches

Future Trends in AI Cybersecurity

  • Emerging techniques in securing AI and machine learning
  • Opportunities for innovation in AI cybersecurity
  • Preparing for future AI security challenges

Summary and Next Steps

Requirements

  • Basic knowledge of machine learning and AI concepts
  • Familiarity with cybersecurity principles and practices

Audience

  • AI and machine learning engineers looking to improve security in AI systems
  • Cybersecurity professionals focusing on AI model protection
  • Compliance and risk management professionals in data governance and security
 14 Hours

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