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An AI system is safe to connect to critical infrastructure only when its risks are acceptable in a defined operational context—not because the model is generally described as safe. Make the decision by assigning accountable owners, documenting what the system can read, recommend, or control, mapping consequences and dependencies, testing representative normal and failure conditions, proving human intervention and safe fallback, and monitoring the connection after approval. NIST’s AI Risk Management Framework (AI RMF) provides a voluntary structure for this work; it is not a universal certification or guarantee.
What “safe” means in a critical-infrastructure connection
“Safe” is a claim about one AI system, one model and version, one interface, and one operating environment. The same model could be acceptable for drafting a maintenance report and unacceptable for recommending protective actions, prioritizing emergency dispatch, or issuing commands to operational technology. The difference is the system’s authority, the consequences of error, the speed at which people must respond, and whether operations can continue when the AI is wrong or unavailable.
Define the connection in concrete terms:
- Read access: which sensors, logs, documents, images, control-system data, or external feeds the AI can receive.
- Decision influence: which human decisions may rely on its output, including cases where the AI cannot actuate equipment directly.
- Control authority: whether it can write data, call an operational interface, change a setting, or trigger an automated workflow.
- People and services affected: operators, customers, patients, passengers, residents, workers, emergency responders, and dependent services.
- Operating conditions: normal, abnormal, degraded, emergency, maintenance, offline, and adversarial conditions.
For infrastructure, evaluate safety, reliability, cybersecurity, privacy, resilience, and other impacts together. Improving one dimension can create a trade-off in another; for example, adding data collection may improve diagnosis while increasing privacy or confidentiality exposure.
Use NIST AI RMF as a structure, not a pass certificate
The National Institute of Standards and Technology’s AI RMF is voluntary and use-case agnostic. Its four functions—Govern, Map, Measure, and Manage—organize risk work, but they do not certify a system or establish that it is suitable for every facility. NIST says the framework should be applied to the particular context and that the greatest risks require the most thorough attention.
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NIST is also developing a profile for trustworthy AI in critical infrastructure. That project is intended to help operators communicate requirements across AI and infrastructure lifecycles and supply chains. It remains a work in development, so treat it as evolving guidance rather than a completed checklist or mandatory universal requirement.
Step 1: Establish governance and decision rights
Before technical testing, decide who is accountable for the connection and who has authority to stop it. A high-consequence deployment should not be approved solely by a model developer, an IT project team, or a procurement function.
Assign named roles
- Accountable executive: accepts or rejects the residual operational risk.
- Operational owner: understands the process, equipment, service levels, and emergency procedures affected by the AI.
- Safety authority: evaluates hazards and confirms that protective controls and fallback modes are adequate.
- Cybersecurity owner: assesses interfaces, identity, segmentation, data integrity, availability, monitoring, and response.
- AI-system provider and suppliers: provide version information, dependencies, update practices, known limitations, and incident contacts.
- Independent approver or review board: challenges assumptions and confirms that evidence matches the proposed operating boundary.
Write down who may authorize initial connection, who may approve a model or data change, who can isolate the system immediately, and how a supplier incident is escalated. Set risk tolerance in terms the operator can act on: which outcomes are unacceptable, which temporary degradations are tolerable, and what evidence is required before exposure increases.
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Inventory high-risk third-party AI systems and their providers. Require notification of material model, software, data, hosting, or interface changes. Define access for audits and incident investigation, retention and deletion rules, service-availability commitments, and a way to obtain a previous model or configuration for rollback. A provider’s general safety statement cannot replace evidence from your own operating context.
Step 2: Describe the intended use and operational boundary
Create a system description that another engineer or regulator could understand without a demonstration account. Record the model name and exact version, system components, prompts or policies, input and output formats, users, locations, connected interfaces, permissions, data sources, hosting arrangement, operating hours, and prohibited uses.
Define allowed behavior
State whether the AI may observe, summarize, rank, recommend, or execute. If it recommends an action, identify the human role that must review it and the information that reviewer must see. If it can call an interface, list every permitted operation and parameter range. Treat a narrow, read-only connection differently from a pathway that can alter a process.
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Define boundaries and assumptions
- List inputs the AI must not use, such as untrusted external content or data from an unapproved tenant.
- State environmental assumptions, including sensor availability, network latency, time synchronization, language, and expected data quality.
- Document conditions outside the validated operating envelope, such as unusual weather, equipment configurations, emergency modes, or a new geographic site.
- Specify what the AI must never do, including autonomous control, unsupervised escalation, or use as the sole source for a safety-critical decision.
The boundary should include indirect influence. An AI that only produces advice can still create harm if operators defer to it, if its recommendations are inserted into a work-order system, or if a downstream automation treats its output as authoritative.
Step 3: Map dependencies, people, and consequences
Trace the complete path from source data to human or machine action. Include data pipelines, preprocessing, model services, hardware, operating systems, identity systems, communications links, cloud or on-premises services, maintenance tools, suppliers, and update channels. Mark every point where loss, delay, corruption, or compromise could propagate.
Analyze impact across operating states
For each important output, ask what happens when it is correct, wrong, late, missing, manipulated, or unavailable. Perform the analysis in at least four states:
- Normal: expected loads, data quality, staffing, and network conditions.
- Abnormal: sensor faults, unusual demand, equipment degradation, incomplete records, or a new pattern.
- Degraded: loss of a data source, intermittent connectivity, reduced staffing, constrained compute, or partial system outage.
- Adversarial: malicious input, unauthorized access, poisoned or altered data, manipulated sensors, and attempts to exploit the AI or its interfaces.
Document the affected service, the time available to detect and respond, the worst credible consequence, and which existing protection prevents escalation. Include people who may be harmed indirectly through service interruption, unsafe instructions, privacy exposure, or operator overload.
Step 4: Identify hazards and failure modes
Build a hazard register rather than relying on a generic list of model limitations. For every hazard, record the trigger, the AI behavior, the operational consequence, the detection method, the preventive control, the recovery action, and the person who owns the residual risk.
Failure modes to test explicitly
- Confidently incorrect output or a recommendation outside the approved operating range.
- Delayed output that arrives after the decision window has closed.
- Missing output, blank output, malformed data, or a service timeout.
- Distribution shift caused by new equipment, seasonal conditions, changed processes, or a different site.
- Sensor, timestamp, labeling, or data-quality errors.
- Conflicting recommendations from multiple systems or unexpected interaction with legacy equipment.
- Operator over-reliance, automation bias, fatigue, or misunderstanding of uncertainty.
- Loss of communications, compute, power, storage, or an upstream supplier service.
- Unauthorized or manipulated inputs, prompt or instruction injection, model extraction, or attempts to infer sensitive training information.
Do not treat a high average accuracy as proof of safety. A rare failure in a low-frequency, high-consequence state can dominate the decision.
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Step 5: Collect evidence with representative testing
Testing should resemble the proposed installation, not a generic benchmark. Use representative data, equipment states, user roles, network paths, latency, workloads, and environmental conditions. Keep the test configuration, model version, data version, prompts or policies, thresholds, and evaluator decisions so results are reproducible.
Measure performance and uncertainty
- Evaluate the outputs that operators will actually use, not only a convenient proxy metric.
- Report performance separately for important operating states, sites, populations, languages, equipment types, and failure conditions.
- Measure false positives, false negatives, omissions, unsafe recommendations, response time, and behavior when inputs are outside the validated range.
- Check whether confidence or uncertainty indicators are calibrated well enough for the intended human decision.
- Identify minimum data quality and availability needed for a valid result.
Test resilience and security
Verify confidentiality, integrity, and availability for the model, its data, interfaces, and supporting services. Assess identity and least-privilege access, network segmentation, secrets management, logging, alerting, backup, restoration, and incident response. Exercise malformed and adversarial inputs, altered data, unauthorized calls, replayed messages, and loss of a dependency. NIST’s AI security work identifies attack surfaces including evasion, model extraction, and membership inference; determine which are relevant to the system and what controls reduce them.
Use simulation and controlled trials
Where live testing could affect service or safety, begin with a digital or hardware-in-the-loop simulation, replay historical cases, and use a shadow mode in which the AI produces outputs without influencing operations. Progress to a tightly bounded pilot only after the failure behavior is understood. A pilot is evidence for that boundary, not proof that a wider connection is safe.
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Step 6: Verify human oversight and usable intervention
“Human in the loop” is meaningful only when the person has time, information, authority, and a workable interface to reject the AI. Specify which role reviews each output, what evidence is displayed, how uncertainty and stale data are shown, and what happens when the reviewer does nothing.
Test realistic workload and stress conditions. An approval control that works in a quiet demonstration may fail when alarms arrive simultaneously, staff are reduced, or the operator is managing an emergency. Confirm that a reviewer can distinguish an AI recommendation from a trusted measurement and can explain or override the resulting action.
Step 7: Prove safe fallback, isolation, and shutdown
Demonstrate what happens when the AI fails, not merely that it can be turned off. Define a fallback mode that preserves essential service or moves the process to a known safe state. Identify who can activate it, how long it takes, what information is lost, and how the system returns to normal operation.
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Required recovery controls
- Independent fallback: a manual, deterministic, or separately validated method that does not depend on the failing AI.
- Isolation: the ability to remove the AI from data and control paths without disabling protections needed by the infrastructure.
- Override authority: an identified person or role with tested credentials and a documented emergency procedure.
- Rollback: a known-good model, configuration, data version, or software release that can be restored.
- Monitoring thresholds: conditions that trigger investigation, degraded mode, or immediate suspension.
- Recovery rehearsal: exercises that include loss of the provider, network, data feed, and operator interface.
NIST AI RMF 1.0 states: “development and deployment should cease in a safe manner until risks can be sufficiently managed.” This is guidance about managing unacceptable negative risk, not a blanket legal prohibition; apply it as a stop-work principle in your governance process.
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Step 8: Make a conditional, documented decision
The approval record should identify the exact model version, operating mode, sites, interfaces, permissions, data sources, test conditions, unresolved risks, compensating controls, residual-risk owners, and review date. A decision can be conditional: for example, read-only access at one site, recommendations limited to a named team, or a pilot that excludes emergency operation.
Decision evidence and blocking signals
| Assessment area | Evidence to retain | Potential block or suspension signal |
|---|---|---|
| Purpose and boundary | System description, allowed and prohibited actions, interface and permission inventory | Unbounded use, undocumented capability, or a path to control outside the approved scope |
| Safety and reliability | Representative scenarios, failure analysis, uncertainty results, and recovery tests | Unacceptable consequence, unknown behavior in a credible operating state, or no effective fallback |
| Cyber resilience | Threat assessment, access review, segmentation, logging, attack testing, and response exercise | Uncontrolled privilege, unverifiable data integrity, exploitable interface, or inability to detect compromise |
| Human factors | Workflow tests, training, override exercise, staffing assumptions, and usability findings | Operators cannot understand, challenge, or override the output in the available time |
| Supply chain and updates | Provider disclosures, dependency inventory, update and rollback procedure | Unannounced material change, missing version traceability, or no reliable support during an incident |
| Operations after launch | Monitoring plan, thresholds, incident contacts, review schedule, and audit trail | No owner for alerts, no stop authority, or no way to reconstruct an important decision |
Do not convert these dimensions into a universal numerical score. NIST’s sources support them as assessment areas, not as a standardized scoring scale. The approving authority must decide whether the remaining risk is acceptable for the specified context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare candidate AI systems on the same evidence
If more than one system is under consideration, run equivalent scenarios and compare the evidence rather than vendor claims. Use the same operating boundary, data conditions, failure injections, human workflow, and security assumptions.
| Comparison axis | Questions to answer |
|---|---|
| Safety and reliability | How does each system behave in normal, abnormal, degraded, and adversarial scenarios? Which failures are detectable? |
| Containment and fallback | Can each system be isolated, overridden, rolled back, or operated safely without it? |
| Cyber resilience | How are interfaces authenticated, segmented, logged, monitored, and recovered after data or service compromise? |
| Performance and uncertainty | What are the limits, error patterns, latency, and uncertainty behavior in the actual environment? |
| Dependencies and suppliers | Which data, hardware, communications, hosting, and update dependencies must remain available or trustworthy? |
| Monitoring and change control | Can operators detect drift, incidents, and unauthorized changes, then suspend or revert the system? |
| Residual risk | Which unresolved risks remain, who accepts them, and what additional evidence or controls are required? |
Monitor continuously after connection
Approval is the start of operational assurance. Monitor the inputs, outputs, latency, availability, error and override rates, data quality, drift, access events, interface calls, provider incidents, and changes in the surrounding process. Set thresholds that lead to investigation, a bounded degraded mode, or immediate isolation. Keep records sufficient to reconstruct what the AI received, produced, and influenced.
Schedule reviews at intervals appropriate to the consequence of failure and whenever a trigger occurs. Reassess after a model, prompt, policy, data source, hardware, network, cloud service, permission, site, connected system, or operating procedure changes. A model update that preserves its name but changes behavior is still a new risk assessment event.
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When to refuse or suspend the connection
Do not connect, or suspend safely, when the organization cannot define the system’s authority, identify an accountable owner, reproduce the tested configuration, protect the data and interfaces, detect unacceptable behavior, or operate without the AI. Suspension is also appropriate when evidence no longer matches the deployed version or environment, a supplier change is unexplained, monitoring is unavailable, or a credible hazard exceeds the approved risk tolerance.
Suspension should follow the pretested procedure: stop the AI’s influence, preserve necessary evidence, move to the independent fallback, notify the operational and safety authorities, assess ongoing impact, and authorize reconnection only after controls and evidence are restored.
Check sector and jurisdiction requirements separately
The applicable obligations depend on the country, infrastructure sector, system function, and deployment architecture. Before approval, consult the relevant regulator, sector authority, safety regime, cybersecurity requirements, privacy rules, procurement terms, and incident-reporting obligations. The AI RMF is voluntary and cannot determine the legal duties for a particular installation. The developing critical-infrastructure profile should likewise be checked for its current status before being relied on.
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A practical assessment record
Use the following headings in the approval file so that the decision remains reviewable:
- System identity, model and software versions, provider, site, owner, and intended purpose.
- Inputs, outputs, users, permissions, interfaces, dependencies, data flows, and prohibited uses.
- Normal, abnormal, degraded, and adversarial operating scenarios.
- Hazards, consequences, controls, assumptions, and residual-risk owners.
- Test datasets and configurations, results, uncertainty limits, security findings, and unresolved failures.
- Human-review workflow, training, override authority, fallback, isolation, rollback, and shutdown exercises.
- Monitoring metrics, alert thresholds, incident contacts, update controls, review dates, and reassessment triggers.
- Conditional approval or rejection, with the accountable decision-maker’s rationale and date.
This record should be specific enough to answer a simple question months later: what exactly was approved, under which conditions, based on which evidence, and who can stop it now?
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