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Duration 14 hours (2 days)
Course Outline
Introduction to Secure and Ethical AI
- Foundations of AI security and ethics
- Typical threats and vulnerabilities in AI systems
- The regulatory environment and compliance frameworks
Security Threats Faced by AI Agents
- Data poisoning and model manipulation
- Adversarial attacks targeting AI models
- Strategies to mitigate AI security threats
Constructing Robust and Secure AI Models
- The secure AI development lifecycle
- Defensive machine learning methodologies
- Validation and testing of AI models
Ethical AI Development and Fairness
- Detecting and mitigating bias in AI models
- Explainability and transparency in AI decision-making
- Guaranteeing responsible AI deployment
AI Governance, Compliance, and Risk Management
- Adhering to GDPR, CCPA, and the AI Act
- Implementing risk management frameworks for AI security
- Auditing AI models for security and ethical integrity
Best Practices for Secure AI Deployment
- Deploying AI agents with a security-focused approach
- Monitoring AI models for anomalies and vulnerabilities
- Responding to and mitigating AI security incidents
Case Studies and Real-World Applications
- Analyzing AI security breaches and extracting lessons
- Implementing secure AI agents in practical scenarios
- Strategies for future-proofing AI security
Summary and Next Steps
Requirements
- A solid grasp of AI and machine learning fundamentals.
- Practical experience with Python and AI frameworks.
- Foundational knowledge of cybersecurity principles.
Target Audience
- AI Developers.
- Security Specialists.
- Compliance Officers.
