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Do not decide that an AI system is “safe” in the abstract. Assess the specific system, version, permissions, interfaces, users, and operating conditions in which it would be used. Before connecting it, identify who is accountable, map hazards and dependencies, test performance and failure behavior under representative conditions, and prove that people can intervene and operations can continue if the AI is wrong or unavailable. Record a conditional go/no-go decision and keep monitoring after deployment.
The NIST AI Risk Management Framework (AI RMF) offers a useful voluntary structure for this work; it is not a universal certification or a guarantee of safety. Applicable legal, sector, safety, and cybersecurity obligations depend on the installation and must be checked separately.
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What “safe to connect” means
Safety is a property of a system operating in a defined context, not a permanent characteristic of a model. A model that performs acceptably in one setting may be unsuitable in another because it has different inputs, users, permissions, connected equipment, or consequences of error. The decision should therefore describe exactly what is being approved and under what limits.
Start by asking what the AI may do: read data, classify events, produce recommendations, change a human operator’s priorities, or directly affect equipment or a service. A tool that cannot actuate equipment can still influence a high-consequence decision; the assessment must account for that human pathway as well as direct control. Consider which systems it can reach, which people or services could be affected, how an error might propagate, and how operations continue if the AI is unavailable or wrong.
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NIST’s AI RMF 1.0 is voluntary and use-case agnostic. Its Govern, Map, Measure, and Manage functions help organize risk work, but applying the framework does not itself approve a connection. NIST is also developing a profile for trustworthy AI in critical infrastructure. That project is intended to help operators apply risk management across infrastructure and AI lifecycles and supply chains; it remains work in development, not a completed certification or universal mandatory checklist.
Set the approval boundary and decision rights
Name accountable people before testing
Assign an operational owner who understands the process the system could affect, an executive accountable for accepting residual risk, a safety authority, a cybersecurity owner, and a technical owner for the AI system. Identify the provider and relevant suppliers as participants in evidence gathering—not as the party that independently approves the operator’s risk. Define who can authorize connection, who can restrict or stop operation, and who must be notified when a failure or supplier issue occurs.
Agree on the organization’s risk tolerance and the decision process before test results arrive. A decision-maker should be able to reject the connection, approve it only within limits, or require additional controls. For high-risk third-party systems, the organization should also define how it will inventory the system, monitor it, and respond to contingencies. These are governance concerns reflected in NIST’s AI RMF Govern function.
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Define what the decision covers
Write down the intended purpose and the boundary of the proposed deployment. Include the model and version, software or service components, data sources, inputs and outputs, intended users, connected interfaces, permissions, operating conditions, and uses that are prohibited. State whether the AI is advisory, can initiate an action for human approval, or can act without a human in the loop.
Approval should attach to this bounded configuration, not to a provider’s product name or a general claim about model capability. If the model, data, environment, permissions, or connected systems change, decide in advance whether that change requires reassessment. The NIST AI RMF Map function emphasizes understanding the use context and helps determine whether AI is appropriate for the use at all.
Map the system, dependencies, and consequences
Trace the operational and technical path
Draw or document the path from the AI’s data sources to its output and from that output to any human or technical action. Include data, software, hardware, communications, cloud or on-premises services, supplier components, and the infrastructure systems that exchange information with the AI. Record where data is transformed, where decisions are made, and where an operator can review or reject an output.
Map access at the level of actual interfaces and permissions. A read-only interface has a different risk profile from a write-capable one, but read-only information can still be used to produce consequential recommendations. Identify dependencies that could affect confidentiality, integrity, or availability, including services outside the operator’s direct control. Note whether loss, corruption, delay, or interruption of any dependency could affect the process.
Identify affected people and services
For each intended use, identify who relies on the output and what could happen if it is incorrect, late, missing, manipulated, or unavailable. Consider safety, reliability, security, privacy, and other impacts together; NIST notes that these dimensions can involve tradeoffs. A change that improves one metric should not be assumed to reduce overall risk if it worsens another operational outcome.
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Include ordinary operation and conditions in which the environment is abnormal or degraded. Examples to examine include sensor or source-data errors, distribution shifts, unexpected interactions with legacy systems, operator over-reliance, and adversarial manipulation. Follow consequences beyond the AI interface: an incorrect recommendation may be repeated, acted upon, or passed into another system, so the analysis should trace plausible downstream effects rather than stop at the model output.
Test the system and its safeguards
Use representative operating conditions
Build a test plan around the deployment context, not solely around general model performance claims. Use representative in-domain data, scenarios, and simulation where appropriate. State the conditions under which each result was obtained, the limits of the test, and what the test does not establish. A result from a controlled test does not by itself prove performance in a different operating environment.
Test normal, boundary, and degraded conditions. Include known failure modes and plausible changes in inputs or operating context. Examine whether outputs remain useful, become uncertain, or fail in ways an operator can recognize. Assess performance limits and uncertainty, including whether users can distinguish a confident-looking but unreliable answer from one supported by the available evidence.
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Check whether inputs, outputs, model components, and supporting data can be accessed or altered in ways that change the system’s behavior. Examine access controls, interfaces, segmentation, monitoring, and incident response as parts of the deployment. NIST’s AI security work identifies confidentiality, integrity, and availability risks for AI systems and their data, and describes emerging attack surfaces such as evasion, model extraction, and membership inference.
For each relevant attack or failure scenario, determine how it would be detected, contained, and reported. Verify that a security response can be carried out without creating a more serious operational hazard. Do not treat a provider’s security statement as a substitute for checking the specific integration, permissions, data flows, and operational response arrangements.
Evaluate the human-AI interaction
Test the workflow as operators will actually encounter it. Determine whether the output is understandable and timely enough for its intended role, whether the operator has the information needed to challenge it, and whether the process creates pressure to accept recommendations without review. Include cases in which the AI is wrong, ambiguous, or silent. If an operator is expected to override or disregard the system, verify that the role, authority, training, and practical means to do so are clear.
Build an evidence package
A reviewer should be able to reproduce the reasoning behind the decision. Keep the system description, architecture and dependency map, hazard analysis, test plan, results, limitations, security findings, and records of operator exercises together. Identify which evidence came from the provider, which came from the operator’s own testing, and which assumptions still need confirmation.
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Ask the provider for dependency and supplier information, update and change-control practices, known limitations, incident-notification arrangements, and support for isolation or rollback. These requests do not transfer accountability to the provider. The operator still has to verify that the supplied evidence applies to the actual installation and that contractual or technical arrangements permit timely response.
Compare candidate systems on the same basis
When more than one AI system is under consideration, test them in the same representative scenarios and compare evidence rather than marketing descriptions. NIST’s materials support the following comparison dimensions, but do not prescribe a standardized numerical score:
| Comparison axis | Questions to answer |
|---|---|
| Safety and reliability | Does each system perform acceptably in normal, abnormal, and degraded scenarios relevant to the process? |
| Failure containment | How does each system fail, how quickly can the failure be detected, and can the function degrade gracefully? |
| Human override | Can authorized staff understand, challenge, override, isolate, or shut down the system in time? |
| Cyber resilience | How are interfaces, credentials, data integrity, segmentation, monitoring, and availability protected? |
| Performance and uncertainty | Are limits and uncertainty visible under the actual operating conditions, including distribution shifts? |
| Dependency transparency | Are cloud, software, hardware, communications, and supplier dependencies documented and manageable? |
| Monitoring and updates | Can the operator detect incidents, control updates, preserve evidence, and reassess a changed system? |
| Residual risk | Which unresolved risks remain, what controls address them, and who has authority to accept them? |
A candidate with higher benchmark performance is not automatically safer if it has weaker isolation, less transparent dependencies, poorer fallback behavior, or update practices that cannot be controlled. Select the system whose complete evidence and operating controls fit the defined boundary.
Common blockers and pitfalls
Stop the approval process when the intended use is vague, the connection boundary is undocumented, or no person has authority to stop operation. A provider’s broad safety claim, a high test score from another environment, or compliance with a general framework is not a substitute for context-specific evidence.
Other common blockers include write permissions that exceed the stated purpose, untested reliance on a cloud or communications dependency, no way to distinguish stale or manipulated data, and a human-review step that exists on paper but cannot work at operational speed. Treat missing incident contacts, uncontrolled model updates, and an untested shutdown path as material gaps rather than administrative details.
Do not assume that a read-only deployment is consequence-free, that encryption alone establishes integrity, or that a successful pilot proves safe scale-up. Reassess when the model, data, environment, permissions, connected systems, supplier, or intended use changes. Security and resilience guidance for AI continues to evolve, so maintain the ability to update controls and repeat testing.
Prove safe fallback, intervention, and shutdown
Before connection, demonstrate how the operation behaves when the AI fails, loses access to a dependency, produces an unacceptable output, or must be isolated. A safe fallback may involve continuing under an established non-AI process, restricting the AI to a less consequential role, or suspending the affected function; the correct response depends on the operational context and must be established by the responsible organization.
Define human intervention and override authority, monitoring thresholds, rollback procedures, and conditions for suspension. Test that isolation or shutdown does not itself cause a worse hazard, and make sure the people expected to act can reach the controls and understand the trigger. NIST AI RMF 1.0 states that “development and deployment should cease in a safe manner until risks can be sufficiently managed.” This is guidance for managing unacceptable negative risk, not a legal prohibition.
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Set out what happens after an incident or material change: who investigates, what evidence is preserved, who is informed, and what must be resolved before operation resumes. Contingency processes should cover relevant third-party failures as well as failures within the operator’s own environment.
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The decision record should make it possible for another qualified reviewer to understand the approval basis without relying on informal assurances. Include the intended use and boundary, system and model version, test conditions and results, identified hazards, controls, unresolved issues, residual-risk owners, approval conditions, and monitoring and review dates. Record the rationale for accepting or rejecting residual risk and the authority that made the decision.
Use an explicit outcome for each material issue rather than treating a list of tests as a pass/fail certificate. The following decision logic keeps the decision tied to evidence:
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|---|---|---|
| Do not connect | A material hazard is not understood, a required control is absent, test evidence does not support the intended use, or residual risk is not acceptable to the accountable authority. | Keep the system out of the proposed operational boundary; resolve the issue and reassess before seeking approval. |
| Approve only with conditions | The evidence supports a bounded use, but safety depends on specified limits, controls, human review, or monitoring. | Document the approved model version, mode, interfaces, permissions, operating limits, control owners, and conditions that would suspend approval. |
| Approve within the stated boundary | Evidence supports the defined use and controls, the fallback and intervention arrangements are workable, and the accountable authority accepts the remaining risk. | Connect only in the approved configuration and maintain the monitoring and review arrangements recorded in the decision. |
Any change to the model, data, environment, permissions, or connected systems should trigger a check against the original assessment and a reassessment where it could alter the risk. Approval is not a once-only conclusion that remains valid regardless of changes.
Monitor after connection and revisit the decision
Deployment changes the evidence base: actual inputs, operator behavior, dependencies, and operating conditions may differ from what was tested. Establish ongoing monitoring appropriate to the system’s role, with named owners for reviewing signals and deciding when to intervene. Track whether the operating conditions remain within the approved boundary and whether the controls and fallback remain available.
Set review points and event-driven triggers. A significant incident, unexpected behavior, drift in relevant inputs, supplier or dependency change, altered permissions, or a change in intended use may justify renewed assessment. Define how the organization will handle updates: who evaluates them, what evidence is required, and whether the current deployment must be paused while the change is assessed. NIST’s AI RMF frames risk management as lifecycle work, rather than a one-time pre-deployment exercise.
Check the obligations that apply to the installation
The AI RMF is voluntary; it does not settle which laws, regulator requirements, sector standards, or safety and cybersecurity obligations apply to a particular installation. Those depend on the country, infrastructure sector, system function, and deployment. The deployment owner should check with the relevant regulator and sector authority and obtain appropriate legal, safety, and cybersecurity review before connection. NIST’s developing critical-infrastructure profile does not replace that step.
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Frequently Asked Questions
Does using the NIST AI RMF certify that an AI system is safe?
No. The AI RMF is voluntary, use-case agnostic guidance. It helps structure governance, context mapping, measurement, and risk treatment, but it does not certify a system or guarantee safety in a particular infrastructure environment.
Can an advisory or read-only AI system bypass the assessment?
No. Recommendations can change operator priorities and affect high-consequence decisions, while read-only data can still be wrong, manipulated, delayed, or unavailable. Assess the full human and operational pathway even when the AI cannot directly actuate equipment.
What changes should trigger a reassessment?
Recheck the assessment when the model or version, data, operating environment, permissions, interfaces, connected systems, supplier, dependency, or intended use changes in a way that could alter risk. A significant incident or unexpected behavior should also trigger review.
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Who makes the final connection decision?
The organization should designate an accountable authority, such as an executive with authority to accept residual risk, supported by the operational owner, safety authority, cybersecurity owner, technical owner, and relevant suppliers. The provider should supply evidence but should not independently approve the operator’s risk.
What must be tested before connection?
Test representative normal, boundary, degraded, and adversarial conditions; known failure modes; uncertainty and performance limits; data and system integrity; access controls; interfaces and segmentation; monitoring; incident response; human override; and safe fallback or shutdown.
What if the AI fails during operation?
Have a documented, tested response that may continue a non-AI process, restrict the AI to a less consequential role, isolate it, or suspend the affected function. Define triggers, authority, communications, rollback, evidence preservation, and conditions for resuming operation.
Are there universal regulations for connecting AI to critical infrastructure?
The applicable requirements depend on the country, infrastructure sector, system function, and installation. Check the relevant regulator and sector authority and obtain appropriate legal, safety, and cybersecurity review; the voluntary AI RMF does not settle those obligations.
Is NIST’s critical-infrastructure AI profile complete?
NIST describes the profile as a project in development intended to help apply risk management across infrastructure and AI lifecycles and supply chains. Treat it as evolving guidance, not a completed certification or mandatory universal checklist.
The Bottom Line
Connect an AI system to critical infrastructure only after a named authority accepts a documented, bounded risk decision supported by representative testing, security and dependency analysis, workable human intervention, and a tested safe fallback. Keep the connection conditional: monitor it, control changes, and suspend or isolate it when assumptions or controls no longer hold.
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