Introduction to AI Governance Frameworks (NIST RMF, AI RMF, ISO/IEC 23894) Fundamentals Training by Tonex
This comprehensive course provides a deep dive into the critical frameworks shaping the responsible development and deployment of artificial intelligence. Mastering these frameworks is essential for navigating the complex landscape of AI, ensuring ethical considerations, and mitigating potential risks. Crucially, understanding these governance models is paramount for cybersecurity professionals, as AI integration introduces new vulnerabilities and necessitates robust security protocols. This course equips you with the knowledge to fortify AI systems against evolving cyber threats and establish secure, compliant AI implementations.
Audience:
- Cybersecurity Professionals
- AI Developers and Engineers
- Data Scientists
- Risk and Compliance Officers
- Legal and Policy Advisors
- Project Managers
Learning Objectives:
- Understand the core principles of AI governance.
- Differentiate and apply NIST RMF, AI RMF, and ISO/IEC 23894.
- Identify ethical considerations in AI deployment.
- Evaluate AI systems for compliance and risk.
- Implement governance strategies for secure AI practices.
- Develop a framework for continuous AI governance.
Course Modules:
Module 1: Foundations of AI Governance
- Introduction to AI Governance Concepts
- Ethical and Legal Considerations in AI
- Overview of Key Regulatory Landscapes
- Understanding AI Risk Management Principles
- The Role of Governance in AI Development
- Impact of AI on Cybersecurity
Module 2: NIST Risk Management Framework (RMF)
- RMF Core Concepts and Components
- Applying RMF to AI Systems
- Risk Assessment and Mitigation Strategies
- Implementation of Security Controls
- Continuous Monitoring and Evaluation
- Integration with AI Lifecycle
Module 3: NIST AI Risk Management Framework (AI RMF)
- AI RMF Structure and Guidelines
- Measuring and Managing AI Risks
- Trustworthiness and Responsible AI
- AI RMF Profiles and Customization
- Practical Application Scenarios
- Addressing Bias and Fairness in AI
Module 4: ISO/IEC 23894: AI Risk Management
- ISO/IEC 23894 Standards and Principles
- Establishing an AI Risk Management System
- Integrating ISO/IEC 23894 with Existing Frameworks
- Compliance and Certification Processes
- Industry Best Practices and Case Studies
- Adapting Standards to Specific AI Applications
Module 5: Implementing AI Governance Strategies
- Developing an AI Governance Plan
- Establishing Roles and Responsibilities
- Data Governance and Privacy Considerations
- Auditing and Accountability Mechanisms
- Creating a Culture of Responsible AI
- Adapting to Evolving AI Technologies
Module 6: Advanced AI Governance Practices
- Emerging Trends in AI Governance
- Advanced Risk Modeling and Simulation
- AI Governance in Specific Industries
- International Collaboration and Standards
- Future of AI Governance and Regulation
- Building a Sustainable AI Ecosystem
Enroll today to become a leader in responsible AI development and deployment.