Effective AI risk management involves many moving parts. While every organization should develop a customized risk approach, established frameworks from organizations like NIST can reduce the learning curve and improve time-to-value.
The NIST AI Risk Management Framework (AI RMF) is a structured playbook that helps identify and mitigate risks throughout the AI risk management lifecycle, serving as a foundation for effective AI risk governance. Compliance is voluntary, but an AI RMF remains a helpful starting point for designing trustworthy AI systems.
Learn why the NIST AI Risk Management Framework is so helpful, as well as its four core functions.
The NIST AI Risk Management Framework is a detailed guidance document released by the U.S. National Institute of Standards and Technology (NIST) for identifying, assessing, and mitigating risks across the full AI risk management lifecycle, including design and development, deployment, and use of AI systems.
Issued in 2023, the NIST AI RMF helps practitioners navigate practical considerations for building safe, reliable, and transparent AI solutions and delivering AI in a manner that aligns with ethical expectations and regulatory requirements.
The AI RMF is a voluntary framework, unlike the EU AI Act (which is law), and is not prescriptive or legally binding. Nevertheless, it is considered global best practice and is already in use by many forward-thinking organizations to provide their AI risk governance program with a clear structure.
The AI RMF has two main components:
The AI RMF is also mapped to international standards, such as ISO/IEC 23894, and was designed to be harmonized with existing frameworks, including the NIST Cybersecurity Framework (CSF), to enable organizations to integrate AI governance considerations into broader risk and compliance efforts. In addition, ISO/IEC 42001 provides an auditable AI management system standard that organizations can adopt alongside AI RMF to formalize their processes.
The AI RMF encompasses four key functions to help organizations better manage AI risks across the AI risk management lifecycle.
First, your organization needs AI risk governance. This stage covers all policies and processes in your organization for deploying AI. It also requires establishing accountability structures that define who makes decisions, how risks are assessed, and which safeguards are in place.
The Govern function helps you create:
Example: An enterprise adds AI oversight to its data protection committee to review model transparency reports and ensure compliance with privacy regulations.
The Map function of the AI RMF helps you understand the purpose and potential impact of each AI system. At this stage, you define:
By mapping these details across every stage of the AI lifecycle, you can identify dependencies and gain a better understanding of risk tolerance.
Every organization is different, but the Map function can help you:
Example: A healthcare provider maps data lineage to verify patient consent across AI workflows, ensuring compliance with HIPAA and GDPR.
The AI Risk Management Framework walks you through the Measure function to evaluate AI system performance. Traditional performance measures, such as accuracy, are important; however, the framework also considers qualitative factors, including fairness and security.
At this stage, you need to create metrics and start tracking your systems’ performance against those metrics. Measuring your performance over time is crucial for spotting bias, drift, and security vulnerabilities long before they cause an issue.
During the Measure function, your team will:
Example: A financial institution utilizes fairness metrics and adversarial testing to identify bias in its credit-scoring models prior to deployment.
The final function of the AI RMF is effective management. At this stage, you take everything you learned from the first three areas and turn these insights into action. Because AI risks can change quickly, the Manage stage ensures your organization can address issues as soon as they appear.
During the final stage, you’ll need to:
Example: A retail company retrains its recommendation model quarterly to prevent drift, reduce bias, and maintain customer trust.
While understanding the NIST AI RMF is a crucial first step, organizations must also actively apply its principles in practice to effectively govern the use of AI. Implementation doesn’t need to be overwhelming. Following the RMF’s recommended phases can help simplify risk management into manageable, actionable steps for any organization.
Maintain an up-to-date inventory of all AI systems, models, and associated automation in use across the organization. This includes in-house built models, open-source libraries, and third-party solutions. Knowing where AI is used and applied in operations is the first step to understanding and mitigating associated risks and regulatory obligations.
Identify a cross-functional AI risk governance team that spans IT, compliance, security, legal, and business functions. Clear ownership is crucial for being accountable for monitoring AI performance, addressing ethical issues, and ensuring adherence to compliance policies. Roles and responsibilities for each AI activity, including approvals, oversight, and escalation paths, should be documented.
Use the Map function of the RMF to comprehensively define the intent, purpose, context, and expected impact of each AI system. Map out the data it uses, the processes and decisions it automates, the stakeholders it serves, and any potential ethical or operational risks it could create, like bias, data drift, or adversarial misuse.
Define key qualitative and quantitative performance metrics that measure the integrity, fairness, and security of AI outputs. Monitor quantitative metrics like accuracy, error rates, model drift, and security logs, as well as qualitative user feedback on trust and transparency. Utilize continuous monitoring tools, such as Mindgard’s AI Artifact Scanning, to automatically identify anomalies and potential risks.
AI systems and their use cases evolve rapidly, so governance and risk management must be equally dynamic. Schedule regular intervals to reassess system behavior, retrain models, and iterate on policies. Establish continuous feedback mechanisms that review audit logs, capture post-incident learnings, and use those insights to improve resiliency over time.
The AI Risk Management Framework is a voluntary standard, but it’s quickly becoming a de facto industry best practice. Regulators around the world are moving to mandate more robust AI governance and management requirements, including in new AI legislation and standards such as the EU AI Act, the U.S. NIST AI RMF, and ISO/IEC 42001.
Early adopters who are already building to these frameworks will be well-positioned to demonstrate accountability and readiness.
Responsible AI will not only avoid future compliance challenges but will also protect your organization’s reputation, customer base, and operating performance. AI systems that are transparent, explainable, and governed according to best practices will have greater trust and confidence from customers, regulators, and investors.
When teams continuously document risk, monitor performance, and act on signals in real-time, they can also demonstrate to stakeholders that they are taking ethics, privacy, and data safety seriously.
To strengthen this foundation, tools like Mindgard’s Offensive Security and AI Artifact Scanning solutions help organizations operationalize AI RMF principles with real-time visibility into model risks, compliance alignment, and performance integrity. Together, they bridge the gap between policy and practice, keeping AI trustworthy from development through deployment. Strengthen AI governance at every stage of the development lifecycle: Get a Mindgard demo now.
No. The AI RMF is a voluntary framework created by NIST, so there are no legal penalties for not following it. However, organizations that adopt it often find it helps with due diligence and with meeting other regulatory requirements.
Yes. NIST intentionally designed the AI RMF to work in conjunction with existing governance programs. Many organizations align it with their NIST Cybersecurity Framework (CSF), ISO 27001, or SOC 2 controls. This approach allows teams to manage AI risk alongside traditional IT and data security measures rather than in isolation.
You don’t need a big team to get started. Begin by mapping your AI systems, identifying key risks, and building a minimal viable governance process. Even a simple checklist or dashboard can help. From there, use tools like Mindgard to automate risk scanning and reduce manual effort.
