NIST AI Risk Management Framework (RMF) Masterclass - 2026

Explore the NIST AI risk management framework, its foundational and core components, and a high-level path to implement governance, controls, and practices that foster trustworthy, technically agnostic AI.

Implement the NIST AI risk management framework by understanding the framework, creating an AI inventory, and applying governance, mapping, measuring, and managing with training and continuous improvement.

Technova applies the NIST AI risk management framework with expert workshops, audits, governance, mapping, measuring, and ongoing monitoring to mitigate risks and build trust.

Explore the foundational concepts of the NIST AI RMF framework and define risk as probability times impact. Examine AI harms to people, organizations, ecosystems, and how risk management mitigates them.

Examine the NIST AI RMF foundation by exploring risk measurement challenges, including transparency gaps, lack of consensus, lifecycle and real-world differences, and human baseline tolerances.

Learn how to set a company's risk tolerance for ai, prioritize unacceptable risks like misdiagnosis, and integrate privacy, cyber security, and governance into ai risk management.

Explore the foundation of trustworthy AI under the NIST RMF, detailing validity, reliability, safety, security, transparency, privacy, and fairness, with a health care benchmark and auditable practices.

Apply the NIST AI RMF core by governing, mapping, measuring, and managing AI risk through actionable items and a governance framework.

Explore how the governance function in the NIST RMF shapes policies, legal compliance, trustworthy AI, risk tolerance, transparency, continuous monitoring, and decommissioning through a Cyber Secure Inc case study.

Establish clear accountability structures and roles for AI risk management, ensure clear lines of communication, build a risk-aware culture through training, and empower executive leadership to strengthen governance.

Govern 3 emphasizes forming a diverse decision-making team for ai risk management to strengthen risk oversight. It defines clear human roles and accountability to sustain governance and risk communication.

Foster a safety-first mindset, document and communicate AI risks, and enable testing and information sharing to promote transparent, trusted AI governance across the organization.

Implement governance five by establishing mechanisms to collect and adjudicate stakeholder and user feedback, and incorporate insights into ai development and deployment.

Develop and implement policies for third party AI risks in supply chain, including due diligence, security standards, and independent audits. Establish contingency plans and scenario planning for third party failures.

Frame the AI risk context with MAP 1, bridging silos through diverse, interdisciplinary teams. Document intended purposes and align with goals, values, and risk tolerance while defining system requirements.

Map 2 defines AI tasks and methods for environmental monitoring, documents knowledge limits with human oversight, and addresses testing and scientific integrity to ensure trustworthy, responsible AI deployment.

Assess the potential benefits and costs of deploying an AI system for environmental monitoring. Define targeted scope, operator proficiency, and human oversight to balance risk and value.

Map four analyzes the risks and benefits of AI system components, focusing on third-party data and software, and documents internal risk controls, audits, and security standards to improve risk management.

Characterize the impact of map five on individuals, groups, communities, and society by identifying, documenting, and engaging stakeholders to integrate feedback and address positive, negative, and unanticipated effects.

Measure 1 introduces how to assess and quantify identified AI risks, select metrics, monitor controls, and evaluate trustworthiness in an urban planning case study.

Explore measure two: document, test, validate, and monitor AI systems for trustworthy characteristics in urban planning. Learn to assess safety, security, privacy, fairness, transparency, explainability, and environmental impact across deployment.

Establish and maintain governance for identifying and tracking AI risks to closure, using regular reviews, risk aging, and end-user feedback to continuously update the risk profile.

Leverage consolidation driven measurement to gather feedback from domain experts, end users, and communities, validate AI trustworthiness through audits, and track post deployment performance.

Assess whether AI systems meet intended purposes, prioritize AI risk treatments, allocate resources, develop and implement mitigation plans, and document residual risks to support transparent risk management.

Manage two components that guide fintech teams to maximize AI benefits and minimize harms through planning, implementing, documenting, feedback, and resource-aware risk assessment with drift monitoring and disengagement protocols.

Discover how fintech companies monitor and control third-party AI risks, implement a risk management program, and enforce controls over third-party data, software, and pre-trained models.

Explore the manage function for risk treatments in fintech, detailing post deployment ai system monitoring, incident response, continual improvements, and transparent communication of incidents to build trust and reliability.

Combine the NIST AI RMF with frameworks like the AWS generative AI security scoping matrix to tailor governance, risk, and resilience controls across five scopes for generative AI workloads.

Extend the NIST AI RMF to agentic AI risks by examining autonomous systems, identifying governance, mapping, measure, and manage functions, and implementing kill switches and human in the loop oversight.

Continue your AI risk management journey by applying the NIST RMF playbook to governance, manage, measure, and map initiatives within your organization.