The NIST AI Risk Management Framework (AI RMF)

The NIST AI Risk Management Framework (AI RMF), available at $44.99, has an average rating of 4.67, with 40 lectures, based on 3 reviews, and has 262 subscribers.

You will learn about Understand the AI RMF structure Identify and map AI risks Measure AI system performance Implement effective risk management Enhance transparency and accountability This course is ideal for individuals who are AI Developers and Engineers: Understand and mitigate AI risks. or Data Scientists and Analysts: Learn about transparency, fairness, and privacy. or Project Managers and Team Leads: Integrate risk management in AI workflows. or Compliance Officers and Legal Professionals: Ensure AI compliance with regulations. or Business Leaders and Executives: Manage AI risks responsibly and sustainably. or Researchers and Academics: Study practical AI risk management frameworks. or IT Professionals: Enhance AI system performance and security. or Policy Makers and Regulators: Develop informed AI governance policies. It is particularly useful for AI Developers and Engineers: Understand and mitigate AI risks. or Data Scientists and Analysts: Learn about transparency, fairness, and privacy. or Project Managers and Team Leads: Integrate risk management in AI workflows. or Compliance Officers and Legal Professionals: Ensure AI compliance with regulations. or Business Leaders and Executives: Manage AI risks responsibly and sustainably. or Researchers and Academics: Study practical AI risk management frameworks. or IT Professionals: Enhance AI system performance and security. or Policy Makers and Regulators: Develop informed AI governance policies.

Enroll now: The NIST AI Risk Management Framework (AI RMF)

Summary

Title: The NIST AI Risk Management Framework (AI RMF)

Price: $44.99

Average Rating: 4.67

Number of Lectures: 40

Number of Published Lectures: 39

Number of Curriculum Items: 40

Number of Published Curriculum Objects: 39

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the AI RMF structure

Identify and map AI risks

Measure AI system performance

Implement effective risk management

Enhance transparency and accountability

Who Should Attend

AI Developers and Engineers: Understand and mitigate AI risks.

Data Scientists and Analysts: Learn about transparency, fairness, and privacy.

Project Managers and Team Leads: Integrate risk management in AI workflows.

Compliance Officers and Legal Professionals: Ensure AI compliance with regulations.

Business Leaders and Executives: Manage AI risks responsibly and sustainably.

Researchers and Academics: Study practical AI risk management frameworks.

IT Professionals: Enhance AI system performance and security.

Policy Makers and Regulators: Develop informed AI governance policies.

Target Audiences

AI Developers and Engineers: Understand and mitigate AI risks.

Data Scientists and Analysts: Learn about transparency, fairness, and privacy.

Project Managers and Team Leads: Integrate risk management in AI workflows.

Compliance Officers and Legal Professionals: Ensure AI compliance with regulations.

Business Leaders and Executives: Manage AI risks responsibly and sustainably.

Researchers and Academics: Study practical AI risk management frameworks.

IT Professionals: Enhance AI system performance and security.

Policy Makers and Regulators: Develop informed AI governance policies.

The “NIST AI Risk Management Framework (AI RMF)” course is designed to equip professionals with the knowledge and tools needed to navigate the complexities of AI risk management effectively. This course delves into the NIST AI RMF, providing a thorough understanding of its principles, functions, and practical applications. Students will explore the MAP, MEASURE, and MANAGE functions, learning how to identify, assess, and mitigate AI risks throughout the AI lifecycle.

Participants will gain insights into the importance of trustworthiness in AI systems, covering key characteristics such as validity, reliability, safety, security, resilience, accountability, transparency, explainability, interpretability, privacy enhancement, and fairness. The course emphasizes the need for a holistic approach to AI risk management, integrating these characteristics to develop robust and trustworthy AI solutions.

Through real-world examples and case studies, including HealthAI, students will see the practical application of the AI RMF in various contexts. The course also covers the importance of continual monitoring and improvement, ensuring that AI systems remain aligned with organizational goals and societal values as they evolve.

This course is ideal for AI practitioners, risk managers, data scientists, and organizational leaders who are involved in the development, deployment, or oversight of AI systems. No prior experience with the NIST AI RMF is required, making it accessible to beginners and valuable to seasoned professionals alike. Join us to master the art of AI risk management and ensure the development of safe, reliable, and ethical AI systems.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: About NIST AI Risk Management Framework (RMF)

Lecture 3: Our Use Case – HealthAI

Chapter 2: AI Risks and Trustworthiness

Lecture 1: Challenges for AI Risk Management(1)

Lecture 2: Challenges for AI Risk Management(2)

Lecture 3: AI Risks and Trustworthiness (1)

Lecture 4: AI Risks and Trustworthiness (2)

Chapter 3: Core Functions of the AI RMF

Lecture 1: Core Functions of the AI RMF

Chapter 4: Govern

Lecture 1: Govern 1.1 .. 1.3

Lecture 2: Govern 1.4 .. 1.7

Lecture 3: Govern 2

Lecture 4: Govern 3

Lecture 5: Govern 4

Lecture 6: Govern 5

Lecture 7: Govern 6

Chapter 5: The MAP Function

Lecture 1: Map 1.1 .. 1.3

Lecture 2: Map 1.4 … 1.6

Lecture 3: Map 2

Lecture 4: Map 3.1 .. 3.3

Lecture 5: Map 3.4 .. 3.5

Lecture 6: MAP 4

Lecture 7: MAP 5

Chapter 6: The Measure Function

Lecture 1: The Measure Function

Lecture 2: Measure 1

Lecture 3: Measure 2.1 .. 2.3

Lecture 4: Measure 2.4 .. 2.6

Lecture 5: Measure 2.7 .. 2.9

Lecture 6: Measure 2.10 .. 2.11

Lecture 7: Measure 2.12 .. 2.13

Lecture 8: Measure 3

Lecture 9: Measure 4.1 .. 4.3

Chapter 7: The Manage Function

Lecture 1: The Manage Function

Lecture 2: Manage 1.1 .. 1.2

Lecture 3: Manage 1.3 .. 1.4

Lecture 4: Manage 2.1 .. 2.2

Lecture 5: Manage 2.3 .. 2.4

Lecture 6: Manage 3

Lecture 7: Manage 4

Chapter 8: Conclusion

Lecture 1: Conclusion

Instructors

Dr. Amar Massoud
Specialized in IT certfication trainings

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5 stars: 2 votes

Frequently Asked Questions

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