Data Privacy Risk Assessment for Nonprofit AI Projects
Before deploying AI systems that process sensitive donor, beneficiary, or employee data, nonprofits must conduct thorough privacy risk assessments to protect trust, ensure compliance, and avoid costly penalties. This guide provides a practical framework for evaluating privacy risks in AI projects and implementing safeguards that protect your organization and the communities you serve.
Nonprofits are increasingly adopting AI tools to improve their operations, from donor retention modeling to case management automation. But AI adoption introduces significant data privacy risks that many organizations have not adequately addressed. In 2026, compliance teams must integrate AI risk assessments into privacy programs to protect trust and avoid penalties, and AI governance is now being judged less by aspirational principles and more by documented processes, controls, and accountability.
The stakes are particularly high for nonprofits. When staff rely on different, unapproved AI tools, organizations expose themselves to data privacy risks, uneven quality of outputs, duplicated costs, and confusion about what is accurate or authoritative. Nonprofits often use free or low-cost AI tools that lack robust security protocols, and it's critical that organizations obtain informed consent for any personal donor information they feed into these systems.
Yet widespread adoption of AI remains early in the nonprofit sector, partly due to concerns around data privacy, accuracy, and staff training. This presents both a challenge and an opportunity. Organizations that establish formal review policies for third-party tools, including vetting vendors for data security practices and ensuring compliance with privacy laws, will maintain data security and build stakeholder trust. Those that fail to implement these safeguards risk exposing sensitive information, violating legal requirements, and damaging relationships with donors and beneficiaries.
This article provides a practical, step-by-step framework for conducting data privacy risk assessments for nonprofit AI projects. Whether you're evaluating a donor management AI system, implementing automated case documentation, or exploring agentic AI tools, you'll learn how to identify privacy risks, evaluate them against relevant compliance frameworks, and implement appropriate safeguards. We'll also explore sector-specific considerations for education nonprofits working with FERPA-protected student data, healthcare organizations managing HIPAA-covered health information, and international nonprofits navigating GDPR requirements.
Why Privacy Risk Assessments Matter for Nonprofits
Privacy risk assessments are not just a compliance formality. They are a systematic process that helps organizations identify and manage privacy risks arising from new projects, initiatives, systems, processes, strategies, policies, and business relationships. For nonprofits implementing AI, these assessments serve multiple critical purposes.
First, they protect the vulnerable populations you serve. Nonprofits frequently work with individuals in sensitive circumstances, including children, refugees, domestic violence survivors, people experiencing homelessness, and individuals with health conditions or disabilities. A privacy breach involving these populations can cause real harm beyond financial loss, including exposure to safety risks, discrimination, or emotional distress. Privacy risk assessments help you anticipate and mitigate these risks before they materialize.
Second, they safeguard donor trust. Donors share personal information and financial data with your organization based on trust. If AI systems mishandle this information through inadequate security, unauthorized disclosure, or inappropriate use, you risk not only losing individual donors but also damaging your organization's reputation. In fact, research shows that 31% of donors report they would give less when organizations use AI, reflecting underlying concerns about data handling and privacy. Conducting thorough privacy assessments demonstrates your commitment to responsible stewardship of donor information.
Third, they ensure regulatory compliance. Many nonprofits are subject to data protection regulations based on their sector, geography, or the populations they serve. Education nonprofits must comply with FERPA. Healthcare nonprofits must follow HIPAA requirements. International nonprofits or those serving European residents face GDPR obligations. State privacy laws like California's CCPA apply to many nonprofits regardless of their tax-exempt status. Privacy risk assessments help you identify which regulations apply to your AI projects and ensure you meet their requirements.
Fourth, they support informed decision-making. Privacy risk assessments force you to examine how AI systems will collect, use, store, and share personal information. This examination often reveals opportunities to reduce data collection, improve security measures, or choose alternative AI solutions with stronger privacy protections. By conducting assessments early in the project lifecycle, you can make adjustments before investing significant resources in implementation.
Finally, they create documentation and accountability. In the event of a privacy incident, data breach, or regulatory investigation, having documented risk assessments demonstrates that your organization took privacy seriously and acted responsibly. This documentation can reduce legal liability, support insurance claims, and help rebuild trust with stakeholders.
The 82% Governance Gap
Research shows that approximately 82% of nonprofit staff use AI tools, but only about 10% of organizations have formal AI policies in place. This massive governance gap leaves organizations exposed to significant privacy risks. Privacy risk assessments provide a structured way to close this gap by establishing documented processes for evaluating and managing privacy implications before deploying AI systems.
Understanding Privacy Risk Assessment Frameworks
Several established frameworks can guide your nonprofit's privacy risk assessments. While no single framework is universally required, understanding the major approaches helps you select or adapt one that fits your organization's needs, resources, and regulatory context.
NIST Privacy Framework
Flexible, outcome-based approach for organizations of any size
The National Institute of Standards and Technology (NIST) Privacy Framework provides a voluntary tool for improving privacy through enterprise risk management. It consists of three parts: Core (privacy outcomes organized into five functions), Profiles (alignment with organizational requirements), and Implementation Tiers (maturity levels).
- Identify-P: Develop understanding of privacy risk for individuals from data processing
- Govern-P: Establish privacy governance and risk management policies
- Control-P: Implement safeguards and manage data throughout lifecycle
- Communicate-P: Maintain transparency about privacy practices
- Protect-P: Safeguard data and respond to events impacting privacy
GDPR Data Protection Impact Assessment
Required for high-risk processing under GDPR
The General Data Protection Regulation requires Data Protection Impact Assessments (DPIAs) for processing activities likely to result in high risk to individuals' rights and freedoms. The UK Information Commissioner's Office and European Data Protection Board provide detailed DPIA templates and guidance.
- Describe processing and its purposes
- Assess necessity and proportionality
- Identify and assess risks to individuals
- Identify measures to mitigate risks
- Document and review decisions
Federal Agency Privacy Impact Assessments
U.S. government template adapted for nonprofit use
Federal agencies such as the U.S. Department of Justice publish publicly available Privacy Impact Assessment templates that walk through data collection, use, sharing, retention, and risk mitigation. These templates can be adapted for nonprofit organizations.
- What information is collected and why
- How information is used and shared
- How long information is retained
- Security and access controls
- Individual rights and recourse mechanisms
ISO/IEC 29134
International standard for privacy impact assessment
ISO/IEC 29134 provides guidelines for establishing and implementing privacy impact assessment processes. This international standard offers a structured approach that can be applied across different regulatory contexts and organizational sizes.
- Preparation and planning
- Information gathering and analysis
- Consultation with stakeholders
- Documentation and decision-making
- Review and monitoring
For most small to mid-sized nonprofits, adapting elements from these frameworks rather than implementing one in its entirety makes the most practical sense. The key is to establish a consistent methodology that addresses the core components: identifying what data your AI system will process, assessing privacy risks, implementing safeguards, and documenting your decisions.
Core Components of a Privacy Risk Assessment
Regardless of which framework you choose, effective privacy risk assessments for AI projects share several core components. Understanding these elements helps you structure your assessment process and ensure you address all critical privacy considerations.
1. Project Description and Data Mapping
Begin by thoroughly documenting what your AI project does and what data it processes. This foundational step establishes the scope of your assessment and ensures all stakeholders understand the privacy implications.
- Purpose and functionality: What problem does the AI tool solve? What specific functions will it perform?
- Data catalog: What types of personal information will be collected, generated, or analyzed? Include direct identifiers (names, addresses, email) and indirect identifiers (demographic data, behavioral patterns, IP addresses).
- Data sources: Where does the data come from? Is it collected directly from individuals, imported from existing databases, obtained from third parties, or generated by the AI system itself?
- Data flows: How does data move through the system? Map inputs, processing steps, storage locations, outputs, and any sharing with third parties.
- Affected populations: Who are the data subjects? Consider donors, beneficiaries, staff, volunteers, and any other individuals whose information the system processes.
2. Necessity and Proportionality Analysis
Privacy law and best practice require that data collection be necessary for the stated purpose and proportional to the benefits sought. This principle of data minimization is particularly important for AI systems, which often have the capacity to process vast amounts of information.
- Necessity test: Is each data element truly necessary for the AI system to function? Can you achieve the same outcome with less information or less sensitive information?
- Purpose limitation: Will data only be used for the stated purpose, or could it be repurposed for other AI projects or organizational functions?
- Proportionality balance: Do the benefits of the AI project (improved services, operational efficiency, better outcomes) outweigh the privacy intrusion? Are there less privacy-invasive alternatives that could achieve similar results?
- Retention limits: How long will data be retained? Is there a clear retention schedule tied to operational or legal requirements rather than indefinite storage?
3. Privacy Risk Identification
Systematically identify the privacy risks that could arise from your AI project. Consider risks from the perspective of affected individuals, not just risks to your organization. Common AI privacy risks include:
- Unauthorized access or disclosure: Could the AI system or its vendor expose data through security breaches, inadequate access controls, or data sharing practices?
- Lack of transparency: Will individuals understand how their data is being used in AI decision-making? Are AI processes explainable and transparent?
- Algorithmic bias and discrimination: Could the AI system produce biased outcomes that unfairly impact certain groups? Have training data and model outputs been evaluated for bias?
- Inadequate consent: Do individuals have meaningful choice about whether their data is processed by AI? Have they been adequately informed?
- Re-identification: Even if data is pseudonymized or aggregated, could the AI system or its outputs be used to re-identify individuals?
- Function creep: Could the AI system gradually expand beyond its original purpose, processing data in new ways without additional privacy review?
- Loss of control: Do individuals lose meaningful control over their information once it enters the AI system? Can they access, correct, or delete their data?
4. Risk Evaluation and Prioritization
Not all privacy risks carry equal weight. Evaluate each identified risk based on likelihood and impact to prioritize your mitigation efforts. Use a simple risk matrix to categorize risks as low, medium, high, or critical.
- Likelihood assessment: How probable is this risk? Consider factors like the vendor's security track record, the sensitivity of data, the number of access points, and technical safeguards in place.
- Impact assessment: If this risk materializes, what harm could result? Consider financial harm, safety risks, reputational damage, discrimination, emotional distress, and regulatory penalties.
- Vulnerable populations: Give higher priority to risks affecting children, refugees, domestic violence survivors, individuals with health conditions, or other vulnerable groups.
- Regulatory context: Risks that could trigger regulatory penalties or legal liability typically warrant higher priority.
5. Risk Mitigation Measures
For each significant risk, identify specific measures to eliminate, reduce, or manage the risk. Document who is responsible for implementing each measure and the timeline for implementation.
- Technical safeguards: Encryption, access controls, data minimization techniques, pseudonymization, secure data transmission, regular security testing
- Organizational measures: Staff training, vendor contracts with privacy requirements, data governance policies, incident response procedures
- Transparency mechanisms: Privacy notices, consent forms, clear explanations of AI use, individual rights procedures
- Accountability measures: Regular audits, privacy impact reviews, documented decision-making processes, designated privacy responsibilities
6. Documentation and Approval
Document your entire assessment process, findings, and decisions. This documentation serves multiple purposes: demonstrating compliance, supporting organizational decision-making, providing accountability, and enabling future reviews.
- Assessment record: Maintain a complete record including project description, data mapping, risk analysis, mitigation measures, and approval decisions
- Decision rationale: Document why certain risks were accepted and others required mitigation
- Approval process: Obtain sign-off from appropriate stakeholders (leadership, legal counsel, board privacy committee)
- Review schedule: Establish when the assessment will be reviewed and updated (typically annually or when the AI system undergoes significant changes)
Sector-Specific Privacy Considerations
Different nonprofit sectors face unique privacy requirements and considerations when implementing AI systems. Understanding the specific regulations and risks relevant to your sector helps you tailor your privacy risk assessment appropriately.
Education Nonprofits: FERPA Compliance
The Family Educational Rights and Privacy Act (FERPA) safeguards the privacy of student education records and regulates how educational institutions collect, use, and disclose students' personally identifiable information. Education nonprofits implementing AI must carefully navigate FERPA requirements to avoid inadvertent disclosure of protected student data.
AI models trained on student data must be carefully managed to avoid disclosing PII and ensure compliance. Transparency in AI systems is critical, and regular monitoring of AI outputs is necessary to prevent inadvertent disclosure. Organizations should ask:
- Does the AI vendor qualify as a "school official" with legitimate educational interest, allowing FERPA-compliant disclosure?
- Have vendor contracts been reviewed to ensure they include required FERPA provisions about data use, retention, and destruction?
- Can the AI system generate outputs that could inadvertently disclose student information to unauthorized parties?
- Are appropriate safeguards in place to prevent AI from being used to circumvent FERPA's restrictions on directory information or parental consent requirements?
For detailed guidance, see our comprehensive article on Student Privacy and FERPA Compliance for Education Nonprofits Using AI.
Healthcare Nonprofits: HIPAA Requirements
The Health Insurance Portability and Accountability Act (HIPAA) includes mandated standards for the secure electronic storage and transmission of health care information. Healthcare nonprofits must ensure AI systems comply with both the Privacy Rule (governing use and disclosure of protected health information) and the Security Rule (requiring administrative, physical, and technical safeguards).
Organizations should conduct HIPAA risk assessments to identify risks to the integrity, confidentiality, and availability of PHI when used in AI technology. These assessments should be conducted regularly, especially when there are changes to existing processes or technology. Critical considerations include:
- Is a Business Associate Agreement (BAA) in place with AI vendors who will have access to PHI?
- Does the AI system implement required encryption and access controls for PHI in transit and at rest?
- Are audit logs maintained to track who accesses PHI through the AI system and for what purposes?
- Can the AI vendor guarantee that PHI will not be used to train models or for purposes beyond the specific contracted services?
- Is there a clear breach notification process if the AI system experiences a security incident involving PHI?
Learn more in our dedicated article on Healthcare Data Protection: HIPAA Compliance for AI in Healthcare Nonprofits.
International Nonprofits: GDPR Obligations
The General Data Protection Regulation (GDPR) is a European law that establishes protections for the privacy and security of personal data about individuals in European Economic Area countries. U.S.-based nonprofits must comply with GDPR if they process data of EU residents, regardless of where the organization is headquartered.
GDPR imposes particularly strict requirements for AI systems, including requirements for data protection by design and by default, transparency about automated decision-making, and data protection impact assessments for high-risk processing. Key considerations include:
- Have you identified a lawful basis for processing (consent, legitimate interest, contract performance, etc.) and documented it?
- Can individuals exercise their GDPR rights (access, rectification, erasure, portability, objection) within the AI system?
- If the AI system makes automated decisions that significantly affect individuals, have you provided meaningful information about the logic involved and given individuals the right to contest?
- Are appropriate safeguards in place for international data transfers (Standard Contractual Clauses, adequacy decisions, etc.)?
- Have you completed a Data Protection Impact Assessment (DPIA) as required for high-risk processing?
For comprehensive GDPR guidance, see European Donor Data: GDPR Compliance for Nonprofits Using AI and International Data Transfer and AI: Compliance for Global Nonprofits.
All Nonprofits: State Privacy Laws
Many nonprofits mistakenly believe that state privacy laws don't apply to them due to their tax-exempt status. In reality, states like California (CCPA), Oregon, Colorado, Minnesota, Maryland, Delaware, and New Jersey provide no nonprofit exemptions, while other states offer only limited exemptions. This means most nonprofits must comply with applicable state privacy regulations.
State privacy laws generally require organizations to provide privacy notices, honor individual rights (access, deletion, correction), implement reasonable security measures, and in some cases, conduct risk assessments for certain types of processing. When implementing AI, consider:
- Which state laws apply based on your location and the location of individuals whose data you process?
- Does your privacy notice adequately disclose AI use and automated decision-making?
- Can you honor individual rights requests (access to data, deletion requests) given how the AI system processes and stores information?
- Are you selling or sharing personal information in ways that trigger additional requirements or opt-out rights?
For California-specific guidance, see our article on State Privacy Laws Decoded: CCPA and AI Implications for Nonprofits.
Practical Privacy Safeguards for AI Implementation
Once you've identified privacy risks through your assessment, you need to implement practical safeguards to mitigate those risks. The following measures represent best practices that apply across different AI use cases and organizational contexts.
Data Minimization and Purpose Limitation
The data minimization principle requires you to identify the minimum amount of personal data you need to fulfill your purpose, and to only process that information and no more. Before sending data into an AI tool, organizations should strip out identifying details using techniques like pseudonymization, masking, or tokenization, and the principle of data minimization, or sharing only what's absolutely necessary, further reduces the risk of leakage.
- Collect only the data elements truly necessary for the AI function
- Use synthetic or aggregated data to replace raw records when possible
- Establish clear retention schedules and delete data when no longer needed
- Implement technical controls to prevent purpose creep and unauthorized secondary uses
Staff Training and Usage Limitations
For nonprofit organizations specifically, training staff is a must, developing clear limitations on AI usage is essential, and other protocols and safeguards may be important to provide warranted accountability, to protect confidentiality of sensitive information, and to address related data retention aspects.
- Provide comprehensive training on what types of information should never be entered into AI systems
- Establish and communicate clear acceptable use policies for AI tools
- Implement approval processes for new AI tools before staff can use them
- Create simple reference guides showing privacy-safe vs. privacy-risky AI uses
Vendor Due Diligence and Contracts
Establishing a formal review policy for third-party tools, including vetting vendors for data security practices, ensuring compliance with privacy laws, and providing regular staff and volunteer training, is key to maintaining data security for the organization.
- Review vendor security certifications (SOC 2, ISO 27001, etc.)
- Ensure contracts prohibit using your data to train AI models or for other vendors' purposes
- Require clear data retention and deletion commitments
- Include breach notification requirements and liability provisions
- Verify that vendors will support your compliance obligations (responding to individual rights requests, etc.)
Transparency and Individual Rights
Organizations must ensure proper data minimization, consent management, and monitoring so AI developers and teams handle sensitive information responsibly. Transparency mechanisms help individuals understand how their data is being used and exercise their privacy rights.
- Update privacy notices to clearly describe AI use and automated decision-making
- Obtain meaningful informed consent when required (not just blanket permissions)
- Establish processes for individuals to access, correct, or delete their data
- Provide clear explanations of AI-generated decisions that significantly affect individuals
- Create accessible channels for privacy questions and concerns
Technical Security Controls
Technical safeguards form the foundation of privacy protection. Organizations should implement data minimization principles by collecting only what is strictly necessary for the intended AI use cases, and use encryption, access controls, and other security measures.
- Encrypt data in transit and at rest
- Implement role-based access controls limiting who can access AI systems and underlying data
- Maintain audit logs of system access and data processing activities
- Conduct regular security testing and vulnerability assessments
- Use secure data destruction methods when deleting information
Ongoing Monitoring and Review
Privacy risk assessment is not a one-time exercise. Establish ongoing monitoring and regular review processes to ensure privacy safeguards remain effective as AI systems evolve and new risks emerge.
- Schedule annual privacy risk assessment reviews
- Trigger new assessments when AI systems undergo significant changes
- Monitor AI outputs for potential privacy violations or bias
- Track privacy incidents and near-misses to identify systemic issues
- Stay informed about evolving privacy regulations and adjust practices accordingly
Common Privacy Risk Assessment Mistakes to Avoid
Even organizations with good intentions make preventable mistakes when conducting privacy risk assessments for AI projects. Awareness of these common pitfalls can help you avoid them.
Conducting Assessments Too Late in the Project
Many organizations wait until an AI system is nearly implemented before conducting a privacy risk assessment. By that point, significant resources have been invested, making it difficult to change course if the assessment reveals serious privacy concerns. Conduct privacy risk assessments early in the project planning phase, before selecting vendors or committing to specific AI solutions.
Focusing Only on Organizational Risk, Not Individual Harm
Privacy risk assessments should focus primarily on risks to individuals whose data is processed, not just risks to your organization. Ask "How could this harm the people we serve?" rather than only "What could go wrong for us?" This shift in perspective often reveals privacy concerns that organizational-focused risk assessments miss.
Relying Solely on Vendor Assurances
AI vendors often provide general privacy and security documentation, but these materials may not address your specific use case, regulatory requirements, or the sensitive nature of nonprofit data. Conduct independent due diligence, ask specific questions about how your data will be handled, and require contractual commitments rather than accepting vendor marketing claims at face value.
Treating Assessment as a Compliance Checkbox
Some organizations complete privacy risk assessments simply to satisfy a policy requirement, without genuinely using the assessment to inform decision-making. The assessment becomes a formality that has little impact on how AI is actually implemented. To avoid this, ensure assessment findings are presented to decision-makers who have authority to require changes, allocate resources for risk mitigation, or reject risky AI projects.
Overlooking Staff Use of Unapproved AI Tools
Privacy risk assessments often focus on formal AI systems officially adopted by the organization, while ignoring the reality that staff members use free AI tools like ChatGPT, Google Gemini, or other consumer AI services for work tasks. When staff rely on different, unapproved tools, organizations expose themselves to data privacy risks, uneven quality of outputs, duplicated costs, and confusion about what is accurate or authoritative. Address this through clear policies, training, and approved alternatives.
Failing to Update Assessments as Systems Evolve
AI systems change frequently. Vendors release updates, add new features, change their data practices, or are acquired by other companies. Privacy risks identified in an initial assessment may no longer be accurate six months later. Establish triggers for reassessment, such as significant system updates, changes in data processing practices, new regulatory requirements, or privacy incidents.
Building a Privacy-First AI Culture
Data privacy risk assessment is more than a technical compliance exercise. It represents a fundamental shift in how your nonprofit approaches AI adoption, one that prioritizes the privacy and dignity of the people you serve alongside the operational benefits of new technology.
Organizations building AI governance programs in 2026 should begin with infrastructure mapping and governance chartering. This means documenting your current AI tools, data flows, and privacy practices before expanding AI use. By establishing documented processes, controls, and accountability mechanisms early, you create a foundation for responsible AI adoption that can scale as your organization grows.
The 82% governance gap, where most nonprofit staff use AI but few organizations have formal policies, creates both risk and opportunity. Organizations that close this gap through systematic privacy risk assessments, clear policies, and staff training will differentiate themselves. They'll build trust with donors who increasingly scrutinize how nonprofits handle their information. They'll protect vulnerable beneficiaries from privacy harms. They'll avoid costly regulatory penalties and data breaches. And they'll make better AI investment decisions by understanding privacy risks before committing resources.
AI governance in 2026 is being judged less by aspirational principles and more by documented processes, controls, and accountability. Privacy risk assessments provide the documentation, structure, and rigor that regulators, board members, funders, and stakeholders increasingly expect. When you can demonstrate that you systematically evaluated privacy risks, implemented appropriate safeguards, and continue to monitor for emerging concerns, you build credibility and trust.
Start where you are. If you haven't conducted privacy risk assessments for your AI projects, begin with your highest-risk systems, those processing sensitive beneficiary data, health information, children's data, or information about vulnerable populations. Use the frameworks and guidance in this article to structure your assessment. Document your findings and decisions. Implement practical safeguards. Then expand the practice to other AI tools and projects.
Privacy risk assessment doesn't have to be perfect to be valuable. Even a basic assessment that identifies obvious risks, asks critical questions about vendor practices, and results in improved safeguards moves your organization forward. As you gain experience, you can refine your methodology, expand your assessment scope, and build more sophisticated privacy governance practices.
The nonprofits that thrive in the AI era will be those that balance innovation with responsibility, that pursue operational efficiency while protecting privacy, and that see privacy risk assessment not as a burden but as an essential part of their mission to serve communities with integrity and care.
Need Help with Privacy Risk Assessment?
Conducting privacy risk assessments for AI projects can be complex, especially when navigating sector-specific regulations like HIPAA, FERPA, or GDPR. Our team helps nonprofits evaluate privacy risks, implement appropriate safeguards, and build governance frameworks that support responsible AI adoption.