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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteEvaluate an AI research partnership as a specific exchange: what each party contributes, what information and systems they can access, how the work could be misused or disrupted, and what each party will do to protect it. Then document the safeguards, owners, monitoring, and conditions for pausing or ending the exchange. The review should be proportionate to the sensitivity of the work and the consequences of failure.
Define the work and its expected value
Begin with the project itself, not a general rating of the prospective partner. Record the research objective, expected outputs, intended users, funding, each party’s role, and the benefits the collaboration is expected to deliver. Note whether the arrangement involves visiting researchers or travel, international collaboration, products or services, software tools, or funding opportunities.
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This context helps distinguish a material security concern from a theoretical one and makes it possible to weigh risk against the purpose of the work. NIST’s Safeguarding International Science: A Research Security Framework, updated November 21, 2025, organizes review guidance across five categories: researchers; international travel; international collaborations; international requests for products, services, or software tools; and funding opportunities. It emphasizes balanced, mission-focused review and says the purpose of research security is “not to stifle collaborative research, but rather to enable and safeguard it.”
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Build an inventory of information, technology, and access on both sides of the collaboration. For each item, record who can access it, where it will be stored or processed, how it will move between parties, and what will happen to copies, outputs, and derivatives.
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- Research materials: datasets, personal or confidential information, unpublished findings, evaluation results, and derived data.
- AI components: model weights, configurations, prompts or other inputs where relevant, training or fine-tuning data, and outputs.
- Supporting assets: source code, software, credentials, compute, databases, services, and online tools.
- Access paths: accounts, interfaces, remote access, shared workspaces, and any privileges granted to researchers or third parties.
NIST SP 800-47 Rev. 1, published July 20, 2021, treats information exchange as a lifecycle: protection is needed before, during, and after the exchange. Apply that idea to the full research workflow, including access granted during setup and data or model copies retained after a work package ends. NIST says organizations should tailor the guidance to their specific needs and information exchange.
Assess conventional and AI-specific threats
For each asset and access path, consider confidentiality, integrity, and availability: who might see information without authorization, alter a system or research result, or make a service unavailable? Include training data, output data, the AI system, and the hardware and software it depends on.
Then consider attack paths that are specific or especially relevant to machine learning. Depending on the project, these can include evasion, model extraction, membership inference, and tampering with data or models. Service disruption also matters where research depends on an external model or platform. NIST notes that existing frameworks do not comprehensively cover several machine-learning attacks or the complexity of the AI attack surface; a review should therefore identify AI-specific exposures rather than assume that a general cybersecurity checklist covers them all.
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For each credible scenario, record the affected asset, likely consequence to the project or people, existing safeguards, and any unresolved exposure. Keep the analysis tied to the actual data, model, users, and deployment conditions rather than treating every possible attack as equally likely.
Review the partner and the dependency chain
Assess the prospective partner’s security measures and ability to carry them out in practice. Ask how it handles the project’s content, limits access, detects incidents, escalates them, and restores affected services or data. Consider relevant security history and whether the partner can explain and verify its controls.
Extend the review beyond the named institution or company. Identify subcontractors, data sources, model and software providers, plugins, platforms, and compute providers that will handle or affect the work. For each dependency, consider the sensitivity of the information it handles and the consequences if it is compromised, unavailable, changed, or withdrawn.
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NIST’s 2024 AI Risk Management Framework: Generative Artificial Intelligence Profile recommends third-party due diligence, comparative risk criteria, audits, ongoing supplier monitoring, and planning for dependencies and fallback options. These are ways to make the review evidence-based; they are not a guarantee that a partner or supplier is risk-free.
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Resolve privacy, provenance, and rights before sharing
Document where data and other materials came from and what uses are permitted. For personal data, establish the handling, access, retention, and deletion arrangements. For research materials and AI components, resolve whether information may be used for training or fine-tuning, what can be published or disclosed, and who owns or licenses inputs, outputs, and derivatives.
- Can each party demonstrate the source and permitted use of data, code, models, and other contributed materials?
- Are training, fine-tuning, evaluation, and reuse permissions distinct and explicit?
- What attribution, ownership, licensing, and publication terms apply to results and derivatives?
- How will personal information, confidential material, and third-party intellectual property be protected?
NIST’s Generative AI Profile identifies privacy, third-party intellectual-property risk, content provenance, and contract terms as areas to address. If rights or permitted uses are unclear, settle them before transferring materials or enabling access.
Put safeguards, responsibilities, and remedies in writing
The agreement should translate the risk review into obligations that each party can implement and verify. NIST SP 800-47 Rev. 1 addresses agreements for managing information-exchange protection; the Generative AI Profile also recommends contract terms covering content ownership, usage rights, security requirements, provenance, and audit clauses.
- Define permitted access and use, required protections, and each party’s security responsibilities.
- Set verification or audit rights and rules for subcontracting or introducing new dependencies.
- Specify incident escalation, notification, cooperation, and response responsibilities.
- Set retention, return, and deletion expectations for source materials, copies, and derivatives.
- Establish change-control, termination, and transition arrangements, including how access will be revoked and work or data handled if the relationship ends.
Make obligations specific enough that project owners know what to do and security teams can check whether it happened. Where a safeguard cannot be provided, record the resulting exposure and who has authority to accept it.
Compare partners or partnership structures on the same criteria
When there is more than one viable partner or design, use consistent criteria rather than relying on a single overall security label. The comparison below synthesizes NIST research-security and third-party guidance; it is not a published NIST scoring scale.
| Comparison criterion | What to establish | Why it matters |
|---|---|---|
| Sensitivity and volume of shared information | Which materials are shared, how sensitive they are, and how much is exposed. | More sensitive or extensive exchanges can increase the consequences of unauthorized access or misuse. |
| Breadth and privilege of access | Who can reach each asset and what actions their access permits. | Broader or more powerful access can increase exposure if an account or system is misused or compromised. |
| Partner security posture and transparency | What safeguards the partner has, what evidence supports them, and how clearly it explains its practices. | Controls matter only to the extent they are suitable for the work and can be relied upon. |
| Provenance of models, data, software, and infrastructure | Where components and materials come from and what use or modification rights apply. | Unclear origins or permissions can create security, privacy, or intellectual-property exposure. |
| Dependencies and concentration | Which outside providers are involved and what happens if a provider is compromised or unavailable. | Critical dependencies can create a single point of failure or limit recovery options. |
| Incident detection, notification, and recovery | How quickly a problem can be identified, communicated, contained, and recovered from. | Response capability affects the duration and consequences of an incident. |
| Privacy and intellectual-property exposure | What personal or protected material is handled and how rights, reuse, and publication are controlled. | Unresolved use or disclosure can harm people, violate obligations, or impair research rights. |
| Research benefit relative to residual risk | Expected benefits, remaining exposure after safeguards, and who accepts that exposure. | The decision should account for both the value of collaboration and the risks that controls do not eliminate. |
Monitor the arrangement and decide when to pause
Assign owners to watch for changes that could alter the original assessment: new partners or subcontractors, different datasets or models, expanded access, infrastructure changes, or changing threat conditions. Record exceptions, corrective actions, and review dates. Test incident and continuity arrangements, and define a fallback where the work depends on a high-risk system or data source that might need to be disabled.
Close the review with a documented decision: the expected benefit, residual risks, agreed safeguards, accountable acceptance authority, next review date, and conditions that would pause or end the exchange. A review is useful only if it can be revisited when the project changes.
Know what the frameworks do—and do not—decide
NIST AI RMF 1.0, released January 26, 2023, is voluntary guidance, not proof of legal compliance. NIST’s current AI RMF page says the framework is being revised as part of the White House AI Action Plan. Treat it as a reference for organizing risk management, not as a substitute for requirements that apply to a particular project.
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The general method here cannot determine whether a specific collaboration is lawful or acceptable. Export controls, sanctions, privacy rules, research-security mandates, contract terms, classified or controlled information, funder conditions, and institutional policies depend on the jurisdiction, partner, technology, data, funding, and project. Refer those questions to the relevant legal, privacy, export-control, research-security, and technical authorities.
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