ETKİNLİKLER

• Objectives of the training
• Importance of AI in organizational risk management
• Overview of key learning outcomes

2. Conceptual Framework of AI

• Key components of AI technology
• History and evolution of AI
• Differences between traditional systems and AI systems
• Overview of AI applications and their benefits

3. AI Lifecycle and Governance

• Understanding the AI lifecycle stages:
  - Planning and designing
  - Data collection and processing
  - Model building and training
  - Deployment and monitoring
• AI governance principles and policies
• Role of ethical frameworks in AI

4. Risks in AI Systems

• Identification of risks:
  - Technical risks (e.g., reliability, security)
  - Ethical risks (e.g., bias, privacy concerns)
  - Operational and reputational risks
• Specific examples and case studies of AI failures

5. AI Risk Management Strategies

• Overview of risk management frameworks (e.g., NIST AI RMF)
• Establishing an effective risk management program
• Tools and methods for mitigating AI-related risks

6. Internal Audit in AI Context

• Role of internal auditors in managing AI risks
• Skills required for AI audits
• Steps in auditing AI systems
• Using audit frameworks to ensure compliance

7. AI Risk Management in Action

• Case studies and real-world scenarios
• Group exercises to identify and mitigate risks
• Discussion on AI governance implementation challenges

8. Wrap-Up and Action Plan

• Summary of key takeaways
• Steps to enhance organizational AI risk management
• Resources and tools for ongoing improvements