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Duration 14 hours
Course Outline
Introduction to Secure and Ethical AI
- Foundations of AI security and ethics
- Prevalent threats and vulnerabilities in AI systems
- Regulatory environment and compliance structures
Security Threats Facing AI Agents
- Data poisoning and model manipulation tactics
- 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
- Identifying and reducing bias in AI models
- Enhancing explainability and transparency in AI decisions
- Promoting responsible AI deployment
AI Governance, Compliance, and Risk Management
- Compliance with GDPR, CCPA, and the AI Act
- Risk management frameworks for AI security
- Auditing AI models for security and ethical integrity
Best Practices for Secure AI Deployment
- Deploying AI agents with security as a priority
- Monitoring AI models for anomalies and weaknesses
- Responding to and mitigating AI security incidents
Case Studies and Practical Applications
- Analyses of AI security breaches and key takeaways
- Applying secure AI agents in real-world contexts
- Strategies for future-proofing AI security
Summary and Next Steps
Requirements
- Familiarity with AI and machine learning principles
- Practical experience with Python and AI frameworks
- Foundational understanding of cybersecurity concepts
Target Audience
- AI developers
- Security specialists
- Compliance officers
