NIST AI RMF – Govern, Map, Measure, and Manage AI Risk Training by Tonex
NIST AI RMF – Govern, Map, Measure, and Manage AI Risk Training by Tonex provides professionals with a structured understanding of the NIST Artificial Intelligence Risk Management Framework and its four core functions. Participants learn how organizations can establish governance, understand AI risk contexts, evaluate trustworthy AI characteristics, and implement risk treatment strategies across the AI lifecycle.
The course emphasizes accountable decision-making, documentation, stakeholder engagement, monitoring, and continuous improvement. AI risk management also has a direct impact on cybersecurity by helping organizations identify vulnerabilities, misuse scenarios, data exposure, and adversarial threats. Strong AI governance strengthens cybersecurity controls and improves organizational resilience against emerging AI-enabled risks.
Learning Objectives
Upon completion of this course, participants will be able to:
- Explain the purpose, structure, principles, and intended outcomes of the NIST AI Risk Management Framework.
- Apply the Govern function to establish accountability, policies, responsibilities, oversight mechanisms, and organizational AI risk management practices.
- Use the Map function to identify AI system context, stakeholders, impacts, dependencies, assumptions, and potential sources of risk.
- Apply the Measure function to assess trustworthy AI characteristics, performance, reliability, safety, security, fairness, transparency, and uncertainty.
- Use the Manage function to prioritize, respond to, monitor, document, and communicate identified AI risks.
- Integrate cybersecurity considerations into AI risk management to reduce vulnerabilities, adversarial exposure, data risks, and system security weaknesses.
- Develop repeatable processes for documenting AI risks, decisions, controls, monitoring activities, and organizational responsibilities.
- Apply a practical training approach that includes exercises, real-world case studies, and examples of processes and documentation used in AI risk management projects.
Audience
This course is designed for:
- AI Governance Professionals
- AI Risk Management Professionals
- Cybersecurity Professionals
- Information Security Managers
- AI Program and Project Managers
- Risk and Compliance Professionals
- Data Scientists and AI Engineers
- Technology Governance Professionals
- Enterprise Risk Managers
- Internal Audit Professionals
- Privacy and Data Protection Professionals
- Legal and Regulatory Professionals
- Responsible AI Practitioners
- AI Assurance Professionals
- Business and Technology Leaders
Course Modules
Module 1: NIST AI RMF Foundations
- Purpose and scope of the NIST AI Risk Management Framework
- Fundamental concepts of artificial intelligence risk management
- Understanding AI risks, impacts, opportunities, and affected stakeholders
- Overview of trustworthy and responsible AI characteristics
- Relationship between risk management and organizational objectives
- Integrating AI risk management throughout the AI lifecycle
Module 2: Govern AI Risk Management
- Establishing organizational policies for responsible AI governance
- Defining AI risk ownership, accountability, and decision authority
- Developing roles and responsibilities across organizational functions
- Creating policies for AI oversight and risk escalation
- Establishing documentation, reporting, and governance review processes
- Building organizational culture supporting responsible AI risk management
Module 3: Map AI Risk Context
- Establishing context for AI systems and intended purposes
- Identifying stakeholders affected by AI system decisions
- Mapping data sources, dependencies, assumptions, and operating environments
- Identifying potential harms, benefits, limitations, and unintended consequences
- Evaluating organizational, societal, technical, and operational risk factors
- Documenting AI system context for informed risk decisions
Module 4: Measure AI Risk Factors
- Selecting appropriate methods for evaluating AI risks
- Measuring validity, reliability, robustness, safety, and resilience
- Assessing security, privacy, transparency, explainability, and accountability
- Evaluating fairness, bias, uncertainty, and potential harmful outcomes
- Establishing metrics, thresholds, indicators, and assessment criteria
- Documenting measurement results, limitations, assumptions, and residual risks
Module 5: Manage AI Risk Responses
- Prioritizing identified AI risks according to organizational significance
- Selecting appropriate risk mitigation and treatment strategies
- Assigning responsibilities for implementing AI risk controls
- Evaluating tradeoffs between risks, benefits, costs, and performance
- Monitoring residual risks and effectiveness of implemented controls
- Establishing escalation, remediation, communication, and acceptance processes
Module 6: Operationalizing NIST AI RMF
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- Integrating Govern, Map, Measure, and Manage activities
- Developing repeatable organizational AI risk management workflows
- Creating AI risk registers, assessment records, and governance documentation
- Aligning AI risk management with cybersecurity and enterprise controls
- Applying continuous monitoring and periodic AI risk reassessment
- Using exercises, real-world case studies, processes, and project documentation
Strengthen your organization’s ability to identify, assess, govern, and manage emerging AI risks with NIST AI RMF – Govern, Map, Measure, and Manage AI Risk Training by Tonex. Build practical capabilities for trustworthy AI governance, cybersecurity resilience, risk assessment, and responsible AI deployment across the enterprise.