AI governance: seize opportunities, minimize risks

Understanding the opportunities and risks of artificial intelligence

• • Overview of the potential of AI in companies and its practical applications.
  • Identification of key risks such as security gaps, discriminatory decisions and data problems.
  • Importance of AI governance to minimize liability risks, reputational damage and technical failures.

Material risks and appropriate governance measures

• • Reputational risks: Avoiding negative public reactions through transparent and fair AI models.
  • Security risks: Protection against cyber attacks and misuse through robust security measures.
  • Data quality: Ensuring a consistent, up-to-date and complete database for reliable AI results.
  • Operational risks: Measures against system failures, incorrect modeling and unpredictable performance.

Core principles and structures of effective AI governance

• • Establishing clear principles such as transparency, accountability and efficiency.
  • Establishment of organizational structures with defined roles, responsibilities and processes.
  • Integration of governance into existing corporate structures and strategic orientation.

Strategies and approaches for successful implementation

• • Analysis and scoping: Assessment of the current status of AI initiatives and identification of regulatory requirements.
  • Framework development: Creation of a customized governance framework that reduces risks and maximizes opportunities.
  • Use of resources: Securing the necessary human and financial resources for implementation.
  • Monitoring and adaptation: Establishment of processes for the continuous review and improvement of AI governance.