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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 seeking to enhance security in AI systems
- Cybersecurity professionals focused on protecting AI models
- Compliance and risk management professionals in data governance and security
14 Hours
Testimonials (1)
The profesional knolage and the way how he presented it before us
