Evaluate an AI research partnership by mapping what each party contributes, what information and systems each can access, and what could happen if any part is exposed, altered, misused, or unavailable. Then agree on safeguards, responsibilities, monitoring, and incident response in proportion to the project’s sensitivity and potential consequences. The result should be a documented decision about whether and how to collaborate—not a blanket judgment about a partner or a technology.
What should an AI partnership security review cover?
Review the particular exchange, not just the institution’s general reputation or the model’s headline capabilities. A joint project can expose unpublished findings through a shared dataset, a model endpoint, a contractor, a software tool, or access to a compute environment. The relevant risk depends on what is being exchanged, who can reach it, and how it will be used.
NIST’s Safeguarding International Science: A Research Security Framework, updated November 21, 2025, treats review as a way to safeguard productive collaboration. It identifies five engagement categories: researchers; international travel; international collaborations; international requests for products, services, or software tools; and funding opportunities. These categories help identify the kinds of engagement to review; they are not a risk score or a presumption that an international partner is unsafe.
Use the framework alongside project-specific technical and governance review. NIST’s AI Risk Management Framework (AI RMF) 1.0, released in 2023, is voluntary and is being revised, according to NIST’s current status information in 2026. It can provide a vocabulary and organizing reference, but it is not a legal-compliance determination.
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How do you conduct the review?
1. Define the purpose, expected outcomes, and participants
Write down the research question, intended users, expected outputs, funding, each party’s role, and the anticipated benefits. Identify whether the engagement involves visiting researchers, travel, international collaboration, products or services, software tools, or funding arrangements. Record what the parties expect to publish, deploy, license, or otherwise do with the results.
Understanding both the research and its potential outcomes makes it possible to weigh security concerns against the value of the work. The review should protect legitimate research while identifying concrete risks, rather than treating collaboration itself as a threat.
2. Inventory assets, exchanges, and access
List what will be shared, used, or made reachable. Include more than files: an AI project may expose information through a model, an API, a development environment, or a connected service.
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- Research materials: datasets, personal or confidential information, unpublished findings, code, model weights, configurations, evaluation results, and documentation.
- Operational access: credentials, administrative privileges, compute, databases, software, services, online tools, and development or testing environments.
- Derived materials: transformed datasets, embeddings, logs, prompts, fine-tuned models, checkpoints, outputs, and evaluation artifacts, where applicable.
For each item, record who can access it, the purpose and level of access, where it is stored or processed, how it moves between parties, and what happens to copies and derivatives. Distinguish read-only access from permission to download, modify, train on, or redistribute material. NIST SP 800-47 Rev. 1 (2021) frames information-exchange protection as necessary before, during, and after an exchange; it also says organizations should tailor its guidance to their needs and requirements.
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Assess confidentiality, integrity, and availability: could information be disclosed, could data or systems be changed without authorization, or could a service or research workflow be disrupted? Apply those questions to the AI system, its training and output data, and the underlying hardware and software.
Then consider AI-relevant paths that may not be captured by a conventional system checklist. Depending on the project, these include evasion (inputs crafted to cause unwanted behavior), model extraction, membership inference (attempts to infer whether a person’s data was in training), data or model tampering, and service disruption. NIST cautions that current frameworks do not comprehensively address several machine-learning attacks or the complexity of AI attack surfaces. Treat a framework checklist as a starting point, not proof that these threats have been covered.
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4. Examine the partner and the dependency chain
Review the partner’s relevant security measures, access controls, content-handling practices, track record, incident history, and ability to detect, report, and recover from an incident. Ask how the partner manages subcontractors and other dependencies, including data sources, platforms, plugins, and compute providers. A sound assessment considers both the partner directly and the parts of the project on which the partner relies.
Compare alternatives using the same criteria rather than relying on a single overall impression:
| Comparison area | What to establish | Useful review question |
|---|---|---|
| Information exposure | Sensitivity and volume of shared or accessible material | What is the most sensitive information this arrangement makes reachable? |
| Access | Who has access, its scope, and its privilege level | Can access be narrowed to the people, systems, and period needed? |
| Partner security | Relevant safeguards, transparency, incident handling, and recovery capability | Can the partner explain and demonstrate how it protects this project’s materials? |
| Provenance | Origins and handling of models, data, software, and infrastructure | Can the project trace the components and sources that matter to its risk? |
| Dependencies | Subcontractors, providers, concentration, and fallback options | What happens to the work if a critical supplier is compromised or unavailable? |
| Privacy and intellectual property | Personal-data exposure, ownership, permissions, and downstream use | Are the permitted uses and rights clear for both inputs and resulting materials? |
| Benefit and residual risk | Expected research value after planned controls, and remaining exposure | Is the remaining risk acceptable to an identified decision-maker? |
These comparison areas synthesize NIST research-security and third-party risk guidance; they are not a separately published NIST scoring scale. Avoid assigning a numerical score unless your organization has a defined method, thresholds, and evidence requirements.
5. Resolve data, privacy, provenance, and rights
Document where project data and model components come from and the permissions governing their use. Address personal-data handling, retention and deletion, derived data, model training or fine-tuning, publication and disclosure, ownership, licensing, attribution, and intellectual-property exposure. Make clear whether a partner may use project materials beyond the agreed work and whether resulting content or model behavior creates third-party rights or privacy concerns.
NIST’s 2024 AI RMF Generative AI Profile highlights third-party intellectual-property and privacy risks, content provenance, and contract terms. The appropriate permissions and legal obligations depend on the actual data, technology, parties, and jurisdictions; a security review cannot settle them by itself.
6. Put protections and responsibilities in writing
Use the agreement and related project documents to make the review actionable. NIST SP 800-47 Rev. 1 addresses agreements for managing information-exchange protection. The Generative AI Profile recommends attention to contracts, including content ownership, usage rights, security requirements, provenance, and audit clauses.
- Define each party’s security responsibilities and permitted access, use, and onward sharing.
- Specify protection requirements for data, models, accounts, environments, and exchanged materials.
- Set rules for subcontracting, supplier changes, and approval of material changes to systems or data use.
- Define retention, return, and deletion duties, including treatment of copies and derivatives.
- Set incident escalation and notification expectations, and identify who coordinates response.
- Establish appropriate audit or verification rights and how findings and corrective actions are handled.
- Provide for termination or transition, including access removal and transfer or deletion of project materials.
Terms should reflect the sensitivity and consequences of the work. For example, a project involving sensitive data may need tighter access, more explicit handling rules, and stronger verification than a collaboration using only public materials.
7. Monitor changes and prepare for disruption
Assign named owners to monitor changes in partners, systems, data, access, suppliers, and threat conditions. Record exceptions and corrective actions so that temporary deviations do not become invisible permanent arrangements. Revisit the assessment when the project adds a dataset, changes a model or provider, expands access, or shifts from research to deployment.
Test incident and continuity arrangements before they are needed. Establish how the parties will contain an incident, preserve relevant information, communicate, and restore or safely pause work. For a high-impact dependency, identify a fallback—or explicitly document why no practical fallback exists. NIST’s Generative AI Profile recommends ongoing supplier monitoring as well as incident and contingency planning. CISA announced a voluntary AI cybersecurity information-sharing playbook on January 14, 2025; consult the current playbook itself before relying on it for operational procedures.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should the organization make the decision?
Close the review with a written decision, not just a list of concerns. Record the expected benefit, identified risks, controls, remaining risk, decision authority, review date, and conditions that require reassessment, pause, or termination. Make clear who accepted any residual risk and on what basis.
A proportionate decision can approve the collaboration as proposed, approve it with conditions, narrow its scope or access, defer it pending evidence or safeguards, or decline it. The choice should reflect the sensitivity of the information, breadth of access, consequences of failure, partner and supplier capability, and expected research value. NIST’s research-security framework emphasizes integrated, mission-focused, risk-balanced review, while also foregrounding privacy and civil liberties.
What this review cannot decide on its own
A general security method does not establish whether a particular partnership is lawful or acceptable under every applicable rule. Export controls, sanctions, privacy requirements, research-security mandates, contract obligations, classified or controlled information rules, funder terms, and institutional policies vary with jurisdiction, partner, technology, data, funding, and project context. Refer those questions to the organization’s legal, privacy, export-control, research-security, and technical authorities. NIST frameworks are guidance; they do not replace binding requirements.
