Securing AI Systems Workshop by Tonex
This workshop explores advanced strategies for safeguarding AI and ML systems against evolving threats. Participants will learn to identify, mitigate, and defend against adversarial attacks on AI, including data poisoning, model evasion, and backdoor vulnerabilities. Gain actionable insights and tools to protect AI applications, ensuring trust and resilience in an AI-driven world.
Learning Objectives:
- Understand the vulnerabilities in AI/ML systems.
- Explore threat models specific to AI applications.
- Learn techniques to counteract adversarial attacks.
- Discover methods to secure training data and models.
- Enhance the robustness and security of AI deployments.
- Apply cybersecurity best practices in AI contexts.
Target Audience:
- AI engineers
- Cybersecurity professionals
- Data scientists
- IT security managers
- Machine learning researchers
- Risk management specialists
Course Modules:
Module 1: Introduction to AI System Security
- Basics of AI and ML vulnerabilities
- Importance of securing AI systems
- Overview of common attack types
- Case studies of compromised AI systems
- Ethical considerations in AI security
- Current security standards in AI
Module 2: Threat Models for AI/ML Systems
- Defining threat models in AI
- Identifying attack surfaces in AI workflows
- Frameworks for evaluating risk
- Security implications of AI applications
- Legal and regulatory concerns
- Future trends in AI threat modeling
Module 3: Adversarial Attacks on AI Systems
- Overview of adversarial attack types
- Data poisoning: strategies and detection
- Model evasion techniques and countermeasures
- Backdoor attacks: prevention and identification
- Real-world examples of adversarial attacks
- Challenges in defending against evolving threats
Module 4: Securing Training Data and Models
- Best practices for securing training datasets
- Tools for data integrity verification
- Protecting against data poisoning
- Model hardening techniques
- Encryption and secure model storage
- Ensuring model explainability and accountability
Module 5: Defense Mechanisms in AI Systems
- Robust training approaches
- Detecting and mitigating adversarial examples
- Implementing anomaly detection systems
- Continuous monitoring for AI threats
- Building resilient AI pipelines
- Strategies for post-attack recovery
Module 6: Practical Applications and Hands-on Training
- Designing secure AI workflows
- Simulating adversarial attacks
- Using tools for AI vulnerability assessments
- Mitigating risks in real-time scenarios
- Collaborative exercises in AI defense
- Building a proactive security strategy
Protect your AI investments. Join Tonex’s Securing AI Systems Workshop to become a leader in AI security. Register today to secure your spot!