Prepare yourself for the 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.

The learning journey

Course Summary Outline

Day 1

Introduction – ISO/IEC Fundamental AI Standards

Day 2

ISO/IEC Specialized AI Standards

Day 3

AI RMF – AI Risks and Trustworthiness

Day 4

NIST AI RMF – Core and Profiles

Day 5

NIST AI RMF: Generative AI (GAI) Profile

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

The Excentra Learning Approach

This course delivers a structured and engaging learning experience through expert-led sessions, interactive discussions, and practical exercises. Participants will analyze case studies of global AI implementations and engage in hands-on workshops applying ISO/IEC standards and the NIST AI RMF to real-world scenarios.

Facilitators combine theoretical insight with practical application, ensuring that learners not only understand the frameworks but also know how to apply them effectively within their organizations. The AI Academy approach empowers participants to lead responsible AI governance initiatives, ensuring systems are safe, secure, and compliant throughout their lifecycle.

Excentra capability map

Six capabilities, and how far this course takes each one

Every Excentra course is mapped against the same six AI capabilities, so you can see at a glance what a course builds and compare it with any other in the catalogue.
01

Data Intelligence

Data-quality requirements under ISO/IEC 5259 applied to one system’s training and input data.
02

Intelligent Monitoring

Risk identification, trustworthiness scoring and Measure-function metrics recorded in a structured register.
03

Document Intelligence

Standards and framework documents are read and compared by hand, not with language models.
04

Workflow & Agent Design

Automated and agentic systems appear as case material for controllability, not as build exercises.
05

Responsible Implementation

Governance structures, lifecycle controls and GAI suggested actions built into the final action plan.
06

Business Transformation

An adoption path for standards and the AI RMF, with owners and sequencing, drafted on day five.

Is This Course Right for You?

This AI Standards and Risk Management Frameworks Course is designed for leaders, technical experts, and policymakers involved in developing, deploying, or governing AI systems. It is especially beneficial for compliance officers, project managers, data scientists, AI engineers, and government professionals responsible for regulatory oversight.

Participants will gain practical understanding of how to apply ISO/IEC standards in real-world contexts and how to operationalize the NIST AI RMF to manage risk across the AI lifecycle. Whether your goal is to strengthen governance, build trust in AI systems, or ensure regulatory alignment, this course provides the global perspective and practical tools you need.

Frequently Asked Questions

The Artificial Intelligence (AI) Standards and Risk Management Frameworks (RMF) training course is intended for leaders, technical experts and policymakers involved in developing, deploying or governing AI systems. It is particularly relevant to compliance officers, project managers, data scientists, AI engineers and government professionals with regulatory oversight responsibilities. Participants will learn how recognised standards and risk frameworks relate to AI governance, implementation and lifecycle management.
The course covers fundamental ISO/IEC AI standards alongside specialised standards for AI control, safety and robustness. Participants will examine AI concepts, terminology, lifecycle management, data quality and decision-making, as well as risk management and testing guidelines for AI-based systems. This knowledge supports professionals responsible for aligning AI design and deployment with internationally recognised approaches to trust, safety and compliance.
The course examines how to apply the NIST AI Risk Management Framework through its four core functions: Govern, Map, Measure and Manage. Participants will learn how these functions support the identification, evaluation and treatment of risks across the AI lifecycle. The content also addresses AI RMF profiles and how organisations can use them to translate risk management principles into policies, procedures and governance practices.
Participants will learn to identify and evaluate risks involving data quality, bias, reliability and explainability in AI systems. The course connects these issues to AI decision-making, transparency and the wider management of systems throughout their lifecycle. This is relevant to professionals who must assess whether AI initiatives are trustworthy, ethically governed and aligned with organisational risk expectations.
Yes, the course specifically addresses the management of Generative AI risks and the application of NIST Generative AI Profile best practices. Participants will consider concerns such as bias, privacy, explainability, reliability and system robustness in the context of AI and Generative AI. This helps those overseeing GAI adoption understand how established risk principles can inform responsible implementation and organisational controls.
The course supports trustworthy AI by focusing on accountability, transparency, ethics, security, safety and robustness. Participants will learn how ISO/IEC standards and the NIST AI RMF can guide responsible decisions during AI design, deployment and lifecycle management. The resulting knowledge can help governance and technical professionals establish practices that make AI risks more visible, structured and manageable.
Yes, the course is designed to help participants assess alignment with international and national AI regulatory frameworks. It explains how globally recognised ISO/IEC standards and the NIST AI RMF can inform compliance reviews, governance expectations and organisational AI policies. The course does not present a specific certification outcome, but it develops knowledge relevant to professionals responsible for oversight, accountability and regulatory alignment.
Technical and project professionals can use the course learning to integrate AI risk considerations into system development, deployment and oversight. Data scientists and AI engineers will gain a governance perspective on data quality, bias, reliability, explainability and robustness, while project managers can relate these issues to lifecycle controls and organisational procedures. This shared framework can support clearer coordination between technical, risk, compliance and policy responsibilities.

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