Artificial Intelligence (AI) Standards and Risk Management Frameworks (RMF)
Mastering ISO/IEC AI Standards and the NIST AI Risk Management Framework for Responsible AI Governance
Prepare Yourself for Artificial Intelligence (AI) Standards and Risk Management Frameworks (RMF) Course
The Artificial Intelligence (AI) Standards and Risk Management Frameworks (RMF) Course equips professionals with the essential knowledge and strategic insight to apply global AI governance and compliance frameworks. As AI adoption accelerates across industries, understanding standards and risk management principles is vital to ensure that AI systems are ethical, transparent, secure, and trustworthy.
This course introduces the most critical ISO/IEC AI standards and provides an in-depth exploration of the NIST AI Risk Management Framework (RMF), including its core functions—Govern, Map, Measure, and Manage. Participants will learn how these frameworks mitigate risks associated with Artificial Intelligence and Generative AI (GAI), addressing challenges such as bias, privacy, explainability, and system robustness.
By the end of the course, professionals will be able to apply recognized standards and frameworks to guide AI implementation responsibly, ensuring alignment with international best practices for trust, safety, and compliance.
Key Learning Outcomes and Objectives?
By completing this course, participants will gain the ability to align AI initiatives with globally recognized standards and frameworks. You will learn to:
- Understand and apply the most critical ISO/IEC AI standards governing AI design and deployment
- Identify and evaluate AI and Generative AI risks, including bias, reliability, and explainability
- Implement the NIST AI RMF across its four functional pillars—Govern, Map, Measure, and Manage
- Develop strategies to ensure trustworthy, ethical, and transparent AI practices
- Assess compliance with international and national AI regulatory frameworks
- Integrate risk management methodologies into organizational AI policies and procedures
- Lead the adoption of responsible AI frameworks that balance innovation with accountability
Course Outline Summary
- Introduction to ISO/IEC fundamental AI standards and key frameworks
- Overview of AI concepts, terminology, and life cycle management
- Data quality, bias, and decision-making in AI systems
- Specialized ISO/IEC standards for AI control, safety, and robustness
- Risk management and testing guidelines for AI-based systems
- Understanding AI Risk Management Framework (AI RMF) principles
- Building trustworthy AI systems through accountability and transparency
- Application of NIST AI RMF core functions – Govern, Map, Measure, Manage
- Exploring AI RMF profiles and their organizational implementation
- Managing Generative AI risks and applying NIST GAI Profile best practices
Accreditation
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