Length: 2 Days
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Artificial Intelligence Security Essentials Training by Tonex

This 2-day course provides a comprehensive introduction to the essential concepts, principles, and practices of securing AI systems. Participants will learn about the unique security challenges posed by AI technologies, how to protect AI models and data, and best practices for ensuring the integrity, confidentiality, and availability of AI systems.

Learning Objectives:

  • Understand the foundational concepts and principles of AI security.
  • Identify and mitigate data security risks in AI systems.
  • Protect AI models from various types of attacks.
  • Implement best practices for securing AI systems and data.
  • Develop and enforce AI security policies and compliance measures.
  • Respond to and recover from AI security incidents effectively.

Target Audience:

  • AI Practitioners and Developers
  • Cybersecurity Professionals
  • Data Scientists and Engineers
  • IT Managers and Directors
  • Compliance and Risk Officers
  • Academics interested in AI Security

Course Outline:

Day 1: Foundations of AI Security

Session 1: Introduction to AI Security

  • Overview of AI and Machine Learning
  • Importance of AI Security
  • AI Security Threat Landscape
  • Key Concepts and Terminology

Session 2: Understanding AI Security Risks

  • Data Security Risks in AI
  • Model Security Risks
  • System and Infrastructure Risks
  • Ethical and Regulatory Considerations

Session 3: Securing AI Data

  • Data Privacy and Protection Techniques
  • Secure Data Collection and Storage
  • Data Anonymization and Encryption
  • Hands-on Session: Implementing Data Security Measures

Session 4: Protecting AI Models

  • Model Training and Evaluation Security
  • Preventing Model Inversion and Extraction Attacks
  • Secure Model Deployment and Monitoring
  • Hands-on Session: Securing an AI Model

Day 2: Advanced Practices and Implementation

Session 5: Adversarial Machine Learning

  • Introduction to Adversarial Attacks
  • Types of Adversarial Attacks (Evasion, Poisoning, etc.)
  • Defense Mechanisms Against Adversarial Attacks
  • Hands-on Session: Implementing Adversarial Defenses

Session 6: AI Governance and Compliance

  • Developing AI Security Policies and Procedures
  • Compliance with AI Regulations and Standards
  • Ethical Considerations in AI Security
  • Case Studies: Governance and Compliance in AI Security

Session 7: Incident Response and Recovery

  • Identifying and Responding to AI Security Incidents
  • Incident Management Frameworks
  • Recovery and Mitigation Strategies
  • Hands-on Session: Creating an AI Incident Response Plan

Session 8: Future Trends and Best Practices

  • Emerging Trends in AI Security
  • Best Practices for Continuous Improvement
  • AI Security Tools and Technologies
  • Final Project: Developing a Comprehensive AI Security Strategy

Final Deliverables:

  • Participants will leave with a comprehensive understanding of AI security risks and mitigation strategies.
  • Hands-on experience in securing AI models and data.
  • Practical insights and best practices for developing and managing AI security initiatives.

Course Materials:

  • Comprehensive Course Workbook
  • Case Studies and Best Practices Documentation
  • Access to AI Security Simulation Tools
  • List of Recommended Reading and Resources

Certification:

Participants will receive a Certificate of Completion upon successfully completing the course and final project.

This course aims to equip participants with the knowledge and skills necessary to secure AI systems, ensuring the integrity, confidentiality, and availability of AI technologies within their organizations.

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