Cybersecurity in AI Systems

• Basic knowledge of machine learning and AI concepts
• Familiarity with cybersecurity principles and practices

Audience

• AI and machine learning engineers looking to improve security in AI systems
• Cybersecurity professionals focusing on AI model protection
• Compliance and risk management professionals in data governance and security

Securing AI systems presents unique challenges that differ from traditional cybersecurity approaches. AI systems are vulnerable to adversarial attacks, data poisoning, and model theft, all of which can significantly impact business operations and data integrity. This course explores key cybersecurity practices for AI systems, covering adversarial machine learning, data security in machine learning pipelines, and compliance requirements for robust AI deployment.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI and cybersecurity professionals who wish to understand and address the security vulnerabilities specific to AI models and systems, particularly in highly regulated industries such as finance, data governance, and consulting.

By the end of this training, participants will be able to:

• Understand the types of adversarial attacks targeting AI systems and methods to defend against them.
• Implement model hardening techniques to secure machine learning pipelines.
• Ensure data security and integrity in machine learning models.
• Navigate regulatory compliance requirements related to AI security.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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