Cybersecurity in AI Systems Training Course
Securing AI systems introduces distinct challenges that diverge from conventional cybersecurity methods. These systems are susceptible to adversarial attacks, data poisoning, and model theft, all of which can severely affect business operations and data integrity. This course examines essential cybersecurity practices for AI systems, addressing adversarial machine learning, data security within machine learning pipelines, and compliance mandates for robust AI deployment.
Designed for intermediate-level AI and cybersecurity professionals, this instructor-led, live training (available online or onsite) focuses on identifying and mitigating security vulnerabilities specific to AI models and systems, with particular relevance to highly regulated sectors such as finance, data governance, and consulting.
Upon completing this training, participants will be able to:
- Identify various types of adversarial attacks targeting AI systems and learn defensive strategies.
- Apply model hardening techniques to safeguard machine learning pipelines.
- Maintain data security and integrity within machine learning models.
- Comprehend and meet regulatory compliance requirements related to AI security.
Format of the Course
- Interactive lectures and discussions.
- Extensive exercises and practical application.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request customized training for this course, please contact us to arrange.
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 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 involved in data governance and security
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Testimonials (1)
The profesional knolage and the way how he presented it before us
Miroslav Nachev - PUBLIC COURSE
Course - Cybersecurity in AI Systems
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