Cybersecurity in AI Systems Training Course

Introduction to AI Security Challenges

• Understanding the unique security risks associated with artificial intelligence systems for government operations.
• Comparing traditional cybersecurity practices with those specific to AI cybersecurity in a government context.
• Overview of potential attack surfaces within AI models used by public sector entities.

Adversarial Machine Learning

• Types of adversarial attacks, including evasion, poisoning, and extraction, and their implications for government systems.
• Implementing effective adversarial defenses and countermeasures to protect government AI models.
• Case studies on adversarial attacks in various industries and their lessons for public sector cybersecurity.

Model Hardening Techniques

• Introduction to enhancing model robustness and hardening for government applications.
• Techniques to reduce the vulnerability of AI models to attacks, ensuring reliability in public sector use.
• Practical exercises with defensive distillation and other methods for hardening AI models for government.

Data Security in Machine Learning

• Securing data pipelines for training and inference to protect sensitive information for government operations.
• Preventing data leakage and model inversion attacks in government AI systems.
• Best practices for managing sensitive data within AI systems used by public sector organizations.

AI Security Compliance and Regulatory Requirements

• Understanding the regulations surrounding AI and data security, particularly in government contexts.
• Ensuring compliance with GDPR, CCPA, and other relevant data protection laws for government entities.
• Developing secure and compliant AI models for government use to meet regulatory standards.

Monitoring and Maintaining AI System Security

• Implementing continuous monitoring strategies for AI systems in the public sector.
• Logging and auditing practices to enhance security in machine learning applications for government.
• Effective response protocols for addressing AI security incidents and breaches within government agencies.

Future Trends in AI Cybersecurity

• Emerging techniques for securing AI and machine learning systems in the public sector.
• Opportunities for innovation in AI cybersecurity to meet evolving threats for government operations.
• Preparing for future AI security challenges and ensuring long-term resilience for government agencies.

Summary and Next Steps