Certified AI Security Leader (CAISL) Workshop

Certified AI Security Leader (CAISL) Workshop by Tonex

This 2-day workshop is designed to provide participants with the skills and knowledge required to become a Certified AI Security Leader (CAISL). Through interactive sessions, hands-on exercises, and collaborative discussions, attendees will learn about AI data security, model security, ethical considerations, adversarial attacks, and explainability in AI systems. The workshop aims to equip AI engineers, data scientists, IT security professionals, and technology leaders with the expertise to lead and manage the security of AI systems and data effectively.

Learning Objectives

• Data Security and Privacy: Understand how to protect sensitive data used in AI systems, comply with data privacy regulations, and prevent data breaches.
• Model Security: Learn how to secure AI models from theft, tampering, or unauthorized access, and ensure robustness against adversarial attacks.
• Ethical Considerations: Explore the ethical implications of AI, including fairness, accountability, and transparency, and learn how to design AI systems that prioritize these values.
• Adversarial Attacks: Gain knowledge on how attackers might manipulate or trick AI systems and learn how to defend against these attacks.
• Explainability: Learn how to design AI systems that can explain their decisions and actions, and understand the importance of interpretability and transparency.
• Leadership in AI Security: Develop leadership skills necessary to manage and oversee AI security initiatives within organizations.

Audience

This workshop is ideal for:

• AI engineers and data scientists involved in AI system development.
• IT security professionals working with AI technologies.
• Technology leaders and managers overseeing AI projects.
• Policymakers and regulators focused on AI ethics and security.
• Any professionals seeking to enhance their leadership skills in AI security and ethical AI development.

Program Details

Part 1:

1. Introduction to AI Security
   • Overview of AI security and its importance
   • Key challenges and considerations in securing AI systems
   • Introduction to the CAISL certification
2. Data Security and Privacy
   • Techniques for protecting sensitive data in AI systems
   • Complying with data privacy regulations (e.g., GDPR, CCPA)
   • Preventing data breaches and ensuring data integrity
3. Hands-on Session: Data Security Implementation
   • Practical exercises in securing AI data
   • Group activities and collaborative security projects
   • Techniques for ensuring data privacy and compliance

Part 2:

1. Model Security
   • Understanding the threats to AI model security
   • Techniques for securing AI models from theft and tampering
   • Ensuring robustness against adversarial attacks
2. Adversarial Attacks
   • Understanding how adversarial attacks work
   • Techniques for defending against adversarial attacks
   • Case studies of adversarial attacks and defenses
3. Hands-on Session: Model Security and Defense
   • Practical exercises in securing AI models
   • Group activities and collaborative defense projects
   • Techniques for enhancing model security

Part 3:

1. Ethical Considerations in AI
   • Understanding the ethical implications of AI
   • Principles of fairness, accountability, and transparency
   • Designing AI systems that prioritize ethical values
2. Explainability in AI Systems
   • Importance of explainability and interpretability in AI
   • Techniques for designing explainable AI systems
   • Tools and frameworks for enhancing AI transparency