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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
- Foundational knowledge of machine learning and AI concepts.
- Familiarity with core cybersecurity principles and practices.
Audience
- AI and machine learning engineers seeking to enhance security in AI systems.
- Cybersecurity professionals specializing in AI model protection.
- Compliance and risk management professionals within data governance and security fields.
14 Hours
Testimonials (1)
The profesional knolage and the way how he presented it before us
