NIST AI Risk Management Framework: A Practical Guide for Nonprofits

The Govern function establishes organizational leadership, oversight, and accountability for AI systems. Unlike the other three functions which apply to specific AI systems, Govern operates at the organizational level, creating the infrastructure within which all AI work occurs. For nonprofits, this means integrating AI governance into your existing organizational structures rather than creating entirely new bureaucracies.

Effective governance begins with clear roles and responsibilities. Who makes decisions about AI adoption? Who monitors AI system performance? Who responds when problems arise? These questions should have clear answers documented in policies accessible to staff, board members, and stakeholders. Your governance structure should reflect your organization's size and complexity, ranging from designated responsibility within existing roles for small organizations to dedicated AI ethics committees for larger nonprofits.

Governance also encompasses creating a culture that values responsible AI use. This means establishing expectations that staff will question AI outputs rather than accepting them uncritically, creating psychological safety for reporting AI-related concerns, and ensuring that your organization's values and mission inform all AI decisions. For many nonprofits, this cultural dimension proves more challenging than technical implementation, requiring ongoing attention and reinforcement.

The Map function focuses on understanding the context in which AI systems will operate. This means identifying who will be affected by the system, what risks might emerge, what benefits you expect to achieve, and how the AI fits into broader organizational processes. Mapping occurs early in the AI lifecycle, ideally before selecting or deploying specific tools.

For nonprofits, mapping should explicitly consider power dynamics and vulnerability. An AI system used to screen job applicants affects people differently than one used to optimize email send times. Systems that influence access to services, evaluate eligibility for assistance, or make decisions about individuals require particularly careful mapping to identify potential harms. The framework encourages you to consider both intended uses and reasonably foreseeable misuses, recognizing that AI systems often get deployed in ways their creators did not anticipate.

Effective mapping engages diverse perspectives. Talk to the people who will use the AI system daily, the people who will be affected by its outputs, and subject matter experts who understand the domain where you'll apply AI. This stakeholder engagement often reveals risks and considerations that technology-focused planning might miss, making it essential for responsible implementation.

The Measure function establishes systematic approaches to assessing whether AI systems operate as intended and whether risks identified during mapping are adequately controlled. Measurement goes beyond simple performance metrics like accuracy or efficiency to encompass trustworthiness characteristics including fairness, reliability, privacy protection, and transparency.

For many nonprofits, measurement represents the most technically challenging aspect of the framework. Evaluating AI systems for bias, testing reliability across different populations, or assessing privacy protections requires capabilities that smaller organizations may lack internally. However, measurement does not always require sophisticated technical analysis. Much valuable measurement comes from systematic observation, user feedback collection, and structured review of AI outputs.

Measurement should be ongoing rather than one-time. AI systems can drift over time as the data they process changes, as external conditions shift, or as users learn to game the system. Regular measurement helps you detect problems early, before they cause significant harm. For resource-constrained nonprofits, this might mean scheduled quarterly reviews rather than continuous monitoring, but the principle of ongoing assessment remains important.

The Manage function focuses on acting based on what you learn through mapping and measurement. When you identify risks, how do you mitigate them? When measurement reveals problems, how do you respond? When incidents occur, how do you prevent recurrence? Management turns assessment into action.

Risk management in AI contexts often requires accepting that perfect safety is unattainable. Instead, you make informed decisions about acceptable risk levels relative to expected benefits. For nonprofits, this risk calculus should explicitly incorporate mission considerations. Some risks may be acceptable for low-stakes applications (like email subject line optimization) that would be unacceptable for high-stakes uses (like matching vulnerable children with foster families).

Effective management also includes planning for things going wrong. What happens when your AI chatbot provides incorrect information to a beneficiary seeking services? How do you respond if your donor prospect scoring system appears to discriminate based on protected characteristics? Having incident response procedures prepared before problems occur allows faster, more effective responses when issues arise.

Many nonprofits lack staff with deep AI or technical backgrounds, making framework implementation seem overwhelming. However, the NIST framework does not require technical expertise to begin. Much of the work involves organizational processes, stakeholder engagement, and structured thinking rather than technical analysis.

Solution: Start with governance and mapping functions, which require subject matter expertise about your programs and mission more than technical knowledge. Leverage vendor expertise by asking AI providers to explain how their systems address NIST framework principles. Partner with technical volunteers, university collaborators, or pro bono consultants for areas requiring specialized knowledge. Focus your internal efforts on decision-making, oversight, and mission alignment rather than technical implementation details.

Nonprofit staff already juggle multiple responsibilities, and adding AI governance to existing workloads can feel impossible. Framework implementation requires time that may seem scarce when balanced against direct service delivery and fundraising demands.

Solution: Implement the framework incrementally, focusing on highest-risk systems first rather than attempting comprehensive coverage immediately. Integrate framework activities into existing meetings and processes instead of creating new bureaucracy. Use the phased timeline suggested earlier, spreading work over 12 months to make resource demands manageable. Remember that preventing AI-related problems through good governance saves time and resources compared to responding to failures after they occur.

Most nonprofits deploy third-party AI tools rather than developing systems internally, creating dependency on vendors for technical details and system modifications. This can make aspects of the framework seem inapplicable when you lack direct control over AI system design.

Solution: Focus on your responsibilities as an AI deployer rather than provider. You control how systems are used, who has access, what decisions they inform, and what human oversight exists. Build vendor accountability into procurement processes by requiring NIST framework alignment as a selection criterion. Develop contingency plans for addressing gaps when vendors cannot meet framework recommendations, including potentially switching providers or discontinuing problematic systems.

The framework encourages measuring trustworthiness characteristics like fairness, transparency, and accountability, which resist simple quantification. Nonprofits may struggle to define meaningful metrics for these abstract concepts.

Solution: Combine quantitative metrics with qualitative assessments. Not everything needs numerical measurement to be meaningful. User feedback, stakeholder interviews, and structured observations provide valuable insight into AI system trustworthiness even without statistical analysis. Start with simple metrics you can actually collect rather than sophisticated measures requiring capabilities you lack. Improvement over time matters more than achieving perfect measurement immediately.