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Before deploying an AI model, confirm that it is fit for a clearly defined business purpose, safe to use with the data and people involved, and supported by real-world testing and operational controls. Readiness is a lifecycle risk-management decision—not a single accuracy score. The checks should scale with the use case, who could be affected, the consequences of an error, the operating environment, and the jurisdictions involved.
1. Define what the AI system will—and will not—do
Assess the complete system, not just the model. A model connected to business data, applications, people, and downstream decisions can create risks that are not visible in a model benchmark alone.
- Write the intended business purpose and name prohibited or out-of-scope uses.
- Map the model, application wrapper, prompts, retrieval sources, data feeds, integrations, users, and any decisions or actions that follow its output.
- Identify who may be affected, how serious a wrong or biased output could be, and whether a decision can be reviewed, reversed, or challenged.
- Name a business owner and technical owner. Assign responsibility for risk acceptance, escalation, and production monitoring.
- Set a risk tier proportionate to the impact and uncertainty. A low-impact drafting assistant does not present the same risk as a system used in hiring, credit, health, or access to essential services.
The NIST AI Risk Management Framework (AI RMF) treats trustworthiness as a concern across design, development, deployment, use, and evaluation. NIST released AI RMF 1.0 on January 26, 2023; it is voluntary guidance, not a certification or a substitute for applicable law. NIST describes the framework as a living document, and its AI Resource Center says it is being revised.
2. Check data governance and privacy before connecting data
Trace the data through the system, including what users submit, what the model retrieves, what the system logs, and what it returns. A provider’s product label alone does not establish that a particular configuration or use is appropriate for your information.
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- Inventory prompts, training or fine-tuning data where relevant, retrieval sources, logs, telemetry, and outputs.
- Check data provenance, permissions, quality, representativeness, retention, access controls, and transfer locations.
- Decide whether personal, confidential, regulated, or sensitive information may be entered. Confirm that the specific configuration and supplier terms permit the intended data use.
- Set rules for data minimization, access, retention, deletion, and handling a suspected exposure.
- Document which data is necessary for the task and which should be excluded, masked, or kept outside the system.
For applicable EU high-risk systems, Article 26 of the AI Act requires deployers to use information supplied under Article 13 to carry out a GDPR or law-enforcement data-protection impact assessment where applicable. Whether this applies depends on the system, the organization’s role, and the circumstances; it is not a blanket requirement for every AI deployment.
3. Test the model against the real task and users
Set acceptance criteria before testing, then evaluate the system in the context where people will actually use it. A strong average score can conceal failures on uncommon but consequential cases or on particular user groups.
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- Define measurable thresholds for the task and intended user population, including which errors are unacceptable.
- Use representative cases as well as edge cases and foreseeable failure scenarios. Record the test sample, method, results, and limitations.
- Check robustness and relevant subgroup performance where differences could affect people or business outcomes.
- For generative systems, test hallucinations, refusal behavior, prompt injection, unsafe or disallowed outputs, and data leakage when relevant to the design.
- Where useful, compare results with the current process or a non-AI baseline to determine whether the system improves the task enough to justify its risks and operating costs.
Decide where human review is needed and make it workable: reviewers should have enough information, time, and authority to correct or reject an output. NIST’s AI RMF guidance treats testing and evaluation as context-specific lifecycle activities, rather than a one-time sign-off.
4. Review security and supplier dependencies
Security review should cover the paths into and out of the system, as well as the model and infrastructure providers on which it depends.
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- Review authentication, authorization, secrets management, network boundaries, logging, and routes through which data or outputs could be exposed.
- Identify providers, subcontractors, infrastructure, software dependencies, model versions, and how updates are delivered.
- Check supplier documentation for intended uses, known limitations, evaluation evidence, data handling and retention, incident notification, and change notification.
- Decide which supplier or model changes require re-evaluation, and how you will respond if a provider changes a model, service, or data practice.
- Compare candidate models or suppliers on the same representative task set and acceptance criteria. Relevant dimensions include task performance and error severity, performance across relevant groups, privacy terms, security and resilience, transparency about limitations and updates, operational support, integration and exit costs, and legal or sector fit.
NIST identifies validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy, and fairness as trustworthiness dimensions. Its framework also points organizations toward cybersecurity and privacy risk practices relevant to AI systems.
5. Put production operations in place before launch
A system that passed pre-release tests can still fail as data, users, suppliers, or the surrounding environment change. Establish how the organization will notice and respond to problems before the system is live.
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- Record baseline performance and choose monitoring signals, alert thresholds, and named owners.
- Track errors, complaints, drift, incidents, unexpected uses, and material changes in the environment or user population.
- Define escalation routes, human override, fallback, rollback, suspension, and retirement procedures. Make clear who can invoke each one.
- Keep an operational record of intended use, model and version, data and configuration, evaluations, approvals, known limitations, incidents, and changes.
- Schedule periodic reassessment and specify what events trigger an earlier review, such as a material update, a new use, or a significant incident.
NIST’s operating guidance includes ongoing monitoring, periodic updates, incident and error tracking, and response. Those controls make deployment an ongoing responsibility rather than a launch-day decision.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Map legal and organizational obligations
Identify the rules that apply to the particular use, not just the technology. Consider where the business operates, where users and data subjects are located, and where consequential decisions are made. Sector rules and internal policies may apply alongside general privacy or AI requirements.
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- Map the use case to relevant laws, regulations, contractual commitments, and internal policies in each jurisdiction involved.
- For EU deployments, determine whether the system is high-risk and whether your organization is acting as a provider, deployer, or in another role. The AI Act sets different duties for different roles; Article 26 addresses deployers of high-risk AI systems, while Article 9 covers risk-management-system requirements.
- Check the effective date for the specific obligation and use area rather than relying on a single general timetable. European Commission guidance says rules for certain high-risk areas, including employment, education, critical infrastructure, and migration, apply from December 2, 2027. The AI Act Service Desk’s displayed Article 9 and Article 26 text is based on the consolidated version as of July 27, 2026; confirm current provisions and dates for compliance decisions.
- Use NIST AI RMF as a voluntary risk-management resource, not as evidence by itself that the system complies with local law.
7. Make a documented go/no-go decision
Before release, bring the owners and reviewers together to decide whether identified risks are acceptable for the intended use. A practical release record should show what was assessed, what remains uncertain, and who is accountable for operating the system.
Quick Recap
- Confirm the intended use, system boundaries, affected groups, owners, and risk tier.
- Review data permissions and handling, context-specific test results, known limitations, and supplier dependencies.
- Verify that required human oversight, monitoring, incident response, fallback, and rollback arrangements are active—not merely planned.
- Record unresolved issues, mitigations, approval authority, and the conditions that would pause or stop deployment.
- Reassess if the use, model, data, supplier, affected population, or applicable requirements materially change.
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