NIST AI Risk Management Framework Essentials Training by Tonex
Build practical fluency with the NIST AI Risk Management Framework (AI RMF) and learn how to operationalize trustworthy AI across strategy, governance, and delivery. This concise program explains the AI RMF core, profiles, and lifecycle integration so teams can align policy, controls, and assurance with business value. You will translate principles into repeatable processes, metrics, and evidence. Cybersecurity impact is emphasized throughout: protecting AI systems from adversarial threats, safeguarding data-in-use, and hardening the MLOps toolchain. You will learn to map AI risks to cybersecurity controls, reduce exposure across models and pipelines, and prepare for evolving AI security regulations.
Learning Objectives
- Explain the purpose, scope, and structure of NIST AI RMF
- Translate AI RMF functions into governance, process, and controls
- Develop AI risk registers, profiles, and assurance artifacts
- Embed risk-informed checkpoints across the AI lifecycle and MLOps
- Measure trustworthy AI with qualitative and quantitative indicators
- Align AI risk management with cybersecurity requirements and demonstrate cybersecurity control coverage
Audience
- AI and data leaders
- Product and engineering managers
- Risk, compliance, and audit professionals
- Security architects and threat analysts
- Cybersecurity Professionals
- MLOps and platform engineers
Course Modules
Module 1 – AI RMF Fundamentals
- AI RMF goals and terminology
- Govern, Map, Measure, Manage functions
- Risk definitions and categories
- Profiles and organizational context
- Trustworthiness characteristics overview
- Stakeholders and responsibility model
Module 2 – Governance And Accountability
- Roles, RACI, and decision rights
- Policies, standards, and procedures
- Risk appetite and tolerance statements
- Model inventory and data lineage
- Third-party and vendor oversight
- Documentation, evidence, and signoffs
Module 3 – Risk Mapping And Profiling
- Use case scoping and impact analysis
- Harm scenarios and misuse mapping
- Risk registers and control objectives
- Contextual profiles for deployment
- Regulatory and standards alignment
- Prioritization and heat mapping
Module 4 – Metrics And Measurement
- Quality, robustness, and resilience KPIs
- Bias, fairness, and drift indicators
- Data, model, and pipeline telemetry
- Assurance tests and thresholds
- Scorecards and continuous monitoring
- Evidence packaging for audits
Module 5 – Risk Management In Practice
- Control design and selection
- Safety mitigations and guardrails
- Secure MLOps and change control
- Incident response for AI failures
- Decommissioning and model retirement
- Reporting to leadership and boards
Module 6 – Trustworthy Deployment And Assurance
- Human oversight and accountability
- Transparency and record keeping
- Privacy-preserving data practices
- Adversarial risk and resilience
- Post-deployment monitoring cadence
- Continuous improvement feedback loops
Elevate your AI program with a proven, auditable approach to risk and trust. Enroll now to apply the NIST AI RMF in your organization, align teams on clear controls and metrics, and deploy AI that is reliable, resilient, and secure.