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Course Outline
Introduction to Secure and Ethical AI
- Fundamentals of AI security and ethics
- Prevalent threats and vulnerabilities within AI systems
- The regulatory environment and compliance frameworks
Security Threats in AI Agents
- Data poisoning and the manipulation of models
- Adversarial attacks targeting AI models
- Strategies for mitigating security threats in AI
Building Robust and Secure AI Models
- The secure AI development lifecycle
- Techniques in defensive machine learning
- Validation and testing protocols for AI models
Ethical AI Development and Fairness
- Detecting and mitigating bias in AI models
- Ensuring explainability and transparency in AI decision-making
- Guaranteeing responsible deployment of AI solutions
AI Governance, Compliance, and Risk Management
- Compliance with GDPR, CCPA, and the AI Act
- Framework for AI security risk management
- Auditing AI models for security and ethical integrity
Best Practices for Secure AI Deployment
- Deploying AI agents with security considerations
- Monitoring AI models to detect anomalies and vulnerabilities
- Incident response and mitigation strategies for AI security
Case Studies and Real-World Applications
- Analysis of AI security breaches and key takeaways
- Implementing secure AI agents in practical scenarios
- Best practices for securing AI for the future
Summary and Next Steps
Requirements
- Proficiency in AI and machine learning concepts
- Hands-on experience with Python and AI frameworks
- Familiarity with core cybersecurity principles
Audience
- AI developers
- Security specialists
- Compliance officers
14 Hours
