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Course Outline
Understanding AI TRiSM
- Introduction to AI TRiSM
- The importance of trust and security in AI
- Overview of AI risks and challenges
Foundations of Trustworthy AI
- Principles of AI trustworthiness
- Ensuring fairness, reliability, and robustness in AI systems
- AI ethics and governance
Risk Management in AI
- Identifying and assessing AI risks
- Mitigation strategies for AI-related risks
- AI risk management frameworks
Security Aspects of AI
- AI and cybersecurity
- Protecting AI systems from attacks
- Secure AI development lifecycle
Compliance and Data Protection
- Regulatory landscape for AI
- AI compliance with data privacy laws
- Data encryption and secure storage in AI systems
AI Model Governance
- Governance structures for AI
- Monitoring and auditing AI models
- Transparency and explainability in AI
Implementing AI TRiSM
- Best practices for implementing AI TRiSM
- Case studies and real-world examples
- Tools and technologies for AI TRiSM
Future of AI TRiSM
- Emerging trends in AI TRiSM
- Preparing for the future of AI in business
- Continuous learning and adaptation in AI TRiSM
Summary and Next Steps
Requirements
- An understanding of basic AI concepts and applications
- Experience with data management and IT security principles is beneficial
Audience
- IT professionals and managers
- Data scientists and AI developers
- Business leaders and policymakers
21 Hours