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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsBefore deploying an AI model, approve a specific combination of model and version, provider, deployment arrangement, intended use, data, and jurisdictions—not just a model name. Review the license and service terms, map every data flow, assess privacy and security separately, test the integrated system, and document who accepts the remaining risk. NIST’s voluntary AI Risk Management Framework (AI RMF) can help organize that work, but it is not a law, certification, contract, or substitute for qualified legal and security review.
1. Define what you are approving
Start with the proposed system and its boundaries. A model may behave differently, carry different terms, or create different exposure depending on its version, host, configuration, integrations, data, and use. Record these details before comparing options.
Write a deployment scope
- Model: provider or publisher, exact model name and version, and whether you will use hosted inference, self-host weights, fine-tune, or combine models.
- Application: intended tasks, users, affected people, level of human review, and what the system is not authorized to do.
- Data: prompts, files, retrieved material, outputs, feedback, telemetry, logs, support records, backups, and any fine-tuning or evaluation datasets.
- Environment: hosting arrangement, connected services, identity controls, integrations, and jurisdictions where data is collected, processed, stored, or accessed.
- Impact and tolerance: plausible harm if the system is wrong, unavailable, manipulated, or exposes information; the controls needed to reduce that risk; and which residual risks the organization will not accept.
Set acceptance criteria that can be checked—for example, which tasks must pass evaluation, which data must never be sent, what human review is required, and which failures block launch. The appropriate thresholds depend on the use and the organization’s risk tolerance; a general framework does not supply a universal safe score.
2. Verify rights for the exact model and use
Do not infer commercial permission, redistribution rights, or unrestricted use from a model’s label, availability, or description as “open.” Read the operative license for the precise version and deployment, along with any acceptable-use policy and other terms incorporated into it. Preserve the documents and version that your decision relies on.
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Review the terms that affect your deployment
- Grant and scope: what rights are granted, to whom, for which materials, and for what uses.
- Commercial use and eligibility: any limits tied to commercial activity, user or organization eligibility, territory, or scale.
- Modification and derivatives: what you may adapt, fine-tune, combine, or distribute, and whether special conditions apply to derivatives or systems improved using model outputs.
- Redistribution: whether you may provide weights, code, or other materials to customers or partners, and what agreement, notice, attribution, or display language must accompany them.
- Acceptable use: prohibited applications, required safeguards, and which policy version governs the service.
- Materials covered: whether model weights, code, documentation, APIs, and related components have the same terms or separate ones.
- Change and termination: how terms can change, what happens to access or rights if a service ends, and what obligations survive.
Meta’s Llama 4 license illustrates why the details matter: it defines “Llama Materials,” grants a limited, non-exclusive, worldwide, non-transferable, royalty-free license under rights Meta owns in those materials, and includes redistribution conditions such as providing the agreement and display or attribution language. It also contains a naming condition for certain distributed models improved using Llama materials or outputs. Those provisions are specific to that license; they do not establish terms for other models or determine whether a particular deployment is permitted. Seek qualified legal review when the terms, intended use, or applicable jurisdictions create material uncertainty.
3. Map data and assess privacy
Treat data handling as a flow through the whole system, not just a question about whether the model provider trains on prompts. The answer depends on the selected provider, product, configuration, and current governing terms. General guidance cannot establish a universal provider practice.
Build a data-flow inventory
For each data category, document where it originates, where it goes, who can access it, how long it is kept, and how it is deleted. Include the model provider, hosting provider, application vendor, subprocessors where disclosed, and your own teams and systems.
Rank #2
- Prompts and conversation history, including personal or confidential information users may enter.
- Uploaded files, retrieved documents, and other context supplied to the model.
- Generated outputs, user ratings, corrections, and feedback.
- Telemetry, application and security logs, backups, and support or troubleshooting records.
- Data used for fine-tuning, evaluation, monitoring, or other service improvement.
For each path, answer from the applicable contract, product settings, and current provider documentation: Is the data used for training or service improvement? What retention and deletion conditions apply? Where is it processed? Which personnel or subprocessors may access it, and under what conditions? Can support access be restricted? What happens to copies in logs and backups? If a provider’s terms do not answer a material question, treat it as unresolved rather than assuming the most favorable answer.
Keep privacy review distinct from security review
For personal information, record the purpose for processing, why each data element is necessary, who is affected, access and retention limits, and foreseeable privacy risks. Minimize what users can submit and what the system retains. Identify the jurisdictions involved and get qualified advice on applicable obligations; this guide does not determine which laws apply.
NIST SP 800-63-4 includes a requirement to perform and document privacy risk assessments for personal information processed by AI/ML systems in identity systems. That requirement is scoped to the identity-system guidance; it should not be presented as a universal legal requirement for every AI deployment.
4. Assess security across the system
A model endpoint is only one part of the attack surface. NIST identifies confidentiality, integrity, and availability concerns involving AI systems and their training or output data, as well as underlying software and hardware. Choose controls for the actual architecture and threat model, including the application and connected services around the model.
Ask for evidence, not just assurances
- Access and isolation: how identities, permissions, tenant or workload separation, and administrative access are controlled.
- Confidentiality: how data is protected in transit and at rest, how secrets are managed, and how exposure through prompts, logs, or support workflows is limited.
- Integrity: how model artifacts, updates, inputs, outputs, and dependencies are protected from unauthorized change or tampering.
- Availability: service resilience, relevant continuity arrangements, and how your application behaves when the model or a dependency is unavailable.
- Software and supply chain: how vulnerabilities are found and addressed, dependencies are managed, and model or service updates are controlled.
- Monitoring and response: what security events are logged, who reviews them, how incidents are reported, and what response commitments apply.
- Deployment-specific testing: how the integrated system handles misuse cases and threats relevant to its tools, retrieval sources, permissions, and data.
Ask which controls the provider operates and which remain your responsibility. Request relevant security documentation and test results, noting their scope, date, and the system or service they cover. Evidence about a provider’s general platform does not by itself show that your configuration, integrations, or application are secure.
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Neither hosted services nor self-hosting is automatically safer or more private. Compare concrete arrangements against the same questions, then assign each control to the party that must operate it.
Rank #4
| Decision area | Hosted model service | Self-hosted or open-weight model |
|---|---|---|
| Rights | Check the service terms, model license, usage policy, and any restrictions on your use or users. | Check the exact model license and separate terms for weights, code, documentation, redistribution, modification, and derivatives. |
| Data control | Verify data categories sent, processing locations and subprocessors where disclosed, retention and deletion terms, training or improvement use, logs, and support access. | Determine what data leaves your environment through hosting, telemetry, integrations, logs, backups, or support, and who can access it. |
| Security responsibility | Establish which provider controls are evidenced and which application, identity, data, and configuration controls you must operate. | Plan who secures infrastructure, access, isolation, secrets, dependencies, artifacts, monitoring, and incident response. |
| Changes and evaluation | Confirm how model or service changes are communicated, whether you can test before changes take effect, and what rollback or configuration choices exist. | Set the process for selecting, testing, updating, monitoring, and rolling back model versions and their dependencies. |
| Operations and cost | Verify current capacity, latency, availability, integration needs, staffing, and total cost from current service terms and quotes; the cited NIST materials do not compare vendor prices or performance. | Estimate infrastructure, operations staffing, capacity, availability, integration, and total cost for your own deployment; the cited NIST materials do not provide comparative cost figures. |
Do not treat an “open-weight” label as a conclusion about license openness, privacy, or security. Likewise, hosted service controls do not remove your responsibility for data selection, application design, access, testing, and oversight.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Evaluate the integrated system before launch
Assess the system you will actually deploy, using the intended model version, configuration, data sources, and integrations. Review documentation and available test results, then run evaluations against both ordinary tasks and plausible misuse or failure cases for this use.
Use a risk-based test plan
- Test the tasks and user workflows named in the deployment scope, including difficult or ambiguous inputs.
- Check whether outputs meet the required quality and whether users can recognize uncertainty or errors where that matters.
- Probe data exposure, unauthorized access, unsafe tool actions, manipulation, and other threats identified for the architecture.
- Test operational failures such as unavailable dependencies, failed retrieval, or a model update that changes behavior.
- Record the model and application versions, test conditions, results, limitations, and who reviewed them.
NIST’s AI RMF organizes trustworthiness across pre-design, design and development, deployment, use, and test and evaluation. Its FAQ describes characteristics including validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy enhancement, and fairness with harmful bias managed. These are considerations, not a guarantee that a system is trustworthy. NIST released AI RMF 1.0 on January 26, 2023, and the Generative AI Profile (NIST AI 600-1) on July 26, 2024. NIST says the framework is being revised, so check its current framework page before relying on its status. The AI RMF and Generative AI Profile are voluntary lifecycle resources, not certification or a substitute for applicable contracts, legal advice, security controls, or organizational risk decisions.
NIST’s AI Risk Management Framework Playbook and AI Risk and Reliability Assessment Portal (AIRC) offer resources for organizing risk actions and testing, evaluation, verification, and validation (TEVV). Use them as aids to a deployment-specific review, not as evidence that your system has passed an independent assessment.
7. Record the decision and revisit it when things change
Keep one approval record that ties the evidence to the exact deployment. It should be usable by product, engineering, privacy, security, procurement, and legal reviewers—not just the team that selected the model.
Decision record checklist
- Approved model, version, provider, deployment arrangement, intended use, data categories, and jurisdictions.
- License, service terms, acceptable-use policy, and other governing documents reviewed, including versions or dates.
- Data-flow map, privacy assessment, security evidence, test plan and results, known limitations, and unresolved questions.
- Controls and owners, launch criteria, human oversight, monitoring approach, incident path, and rollback or shutdown plan.
- Residual risks, rationale for accepting them, approver names or roles, and the date or conditions for review.
Set review triggers for a model or provider change, revised contract or policy, new data category or jurisdiction, changed use or user group, added integration, material incident, or evaluation result that changes the risk picture. NIST’s Generative AI Profile and AIRC can help teams structure lifecycle actions and TEVV, while the organization remains responsible for deciding whether its specific deployment is acceptable.
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