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Course Outline
Introduction to AI Security Challenges for Government
- Understanding the unique security risks associated with AI systems in government operations
- Comparing traditional cybersecurity measures with those specific to AI cybersecurity in public sector environments
- Overview of attack surfaces within AI models used for government applications
Adversarial Machine Learning
- Types of adversarial attacks: evasion, poisoning, and extraction, with a focus on their implications for government systems
- Implementing robust adversarial defenses and countermeasures to protect government AI models
- Case studies examining adversarial attacks in various industries and their relevance to public sector operations
Model Hardening Techniques
- Introduction to enhancing model robustness and hardening for government use
- Techniques to reduce vulnerability of government AI models to potential attacks
- Practical exercises with defensive distillation and other methods to harden AI models for government applications
Data Security in Machine Learning
- Securing data pipelines for training and inference in government AI systems
- Preventing data leakage and model inversion attacks within the public sector
- Best practices for managing sensitive data in AI systems used by government agencies
AI Security Compliance and Regulatory Requirements
- Understanding regulations governing AI and data security for government operations
- Compliance with GDPR, CCPA, and other data protection laws in the context of government AI systems
- Developing secure and compliant AI models for use by government entities
Monitoring and Maintaining AI System Security
- Implementing continuous monitoring strategies for AI systems used in government
- Logging and auditing practices to ensure security in machine learning applications within the public sector
- Effective response protocols for addressing AI security incidents and breaches in government operations
Future Trends in AI Cybersecurity
- Emerging techniques for securing AI and machine learning systems used by government agencies
- Opportunities for innovation in AI cybersecurity within the public sector
- Preparing government organizations for future AI security challenges
Summary and Next Steps
Requirements
- Fundamental understanding of machine learning and artificial intelligence concepts
- Knowledge of cybersecurity principles and practices
Audience
- AI and machine learning engineers seeking to enhance security in AI systems for government
- Cybersecurity professionals specializing in the protection of AI models
- Compliance and risk management professionals focused on data governance and security
14 Hours
