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AI can strengthen critical-infrastructure defense by helping security teams identify vulnerabilities, analyze events and support operational decisions. It cannot be treated as an autonomous shield or as a substitute for engineers, incident responders and safety controls. In operational technology (OT), a bad model, corrupted data or silent process drift can affect physical equipment and essential services. The defensible approach is to use AI as bounded decision support, measure both cyber and physical consequences, maintain human authority to override it, and provide a safe fallback when the system is wrong or unavailable.
What “AI to defend critical infrastructure” actually means
Critical infrastructure includes services such as energy, water and other systems whose disruption can harm the public or the economy. These environments combine information technology (IT), operational technology (OT), industrial processes, communications and safety systems. AI may be inserted into any of those layers, but its role matters more than the label attached to it.
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AI as defensive augmentation
The strongest evidence supports augmentation rather than replacement. NIST says that “AI technologies also have the potential to transform cybersecurity.” It describes AI as a way to give defenders new tools for addressing security vulnerabilities, while warning that the same capabilities can improve the work of people targeting organizations through IT and OT attacks. The Government Accountability Office (GAO) similarly says AI could improve systems operating critical infrastructure such as water and energy while increasing their vulnerability to cyberattacks.
For an operator, augmentation means an AI system may organize large volumes of alerts, identify patterns for an analyst to investigate, help prioritize known weaknesses, or support a human decision about an operational condition. Those are potential uses, not proof that an AI product prevents attacks or performs more safely than a conventional control in every sector.
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What the evidence does not establish
No outcome statistic establishes that AI independently stops attacks against critical infrastructure. The available guidance and assessments also do not show that AI eliminates staffing needs, makes autonomous control safe by default, or is already more effective across all infrastructure sectors. A deployment should therefore be justified by a specific operational problem and measured against a defined baseline, not by the presence of an “AI” feature.
Where AI can help infrastructure defenders
The December 2025 joint guidance on integrating AI into OT describes possible benefits including efficiency, productivity, decision support, cost savings and customer experience. These benefits are conditional: they depend on the quality of the data, the way the model is connected to operational systems and the controls surrounding its use.
Security analysis and vulnerability work
AI can help a security team sort information that would otherwise compete for limited analyst time. A model might surface relationships among vulnerability records, asset information and observed activity for a person to validate. This is useful only when the team can inspect the supporting evidence and correct a mistaken priority. An opaque ranking that quietly omits an important asset can create a false sense of coverage.
Operational decision support
In an OT setting, AI may summarize conditions or suggest a response for a qualified operator. Keeping the recommendation separate from the final control action limits the consequences of an incorrect prediction. The operator still needs a clear explanation of what data was used, when it was collected, how fresh it is and what uncertainty remains.
Efficiency and service planning
Efficiency, productivity and customer-experience improvements can be valuable in public services, but they do not automatically offset safety or availability risk. A small administrative gain is not a reason to expose a protection system to an unnecessary model dependency. The business case should state which service improves, how that improvement will be measured and what level of disruption is acceptable if the AI function is disabled.
Why OT makes AI risk different
IT systems can often be isolated, restored from backups or patched during a maintenance window. OT controls physical processes, so a software error or delayed response may affect equipment, workers, water quality, power delivery or other essential outcomes. The December 2025 guidance identifies process-model drift and the bypassing of safety processes as risks that operators must manage when integrating AI into OT.
Process-model drift
A model is built from assumptions about equipment, operating ranges and relationships among signals. Those assumptions can become wrong as assets age, configurations change, sensors are replaced or operating conditions move outside the training data. A system can continue producing confident recommendations even while its view of the process is no longer accurate. Operators need a way to detect material drift, pause automated recommendations and revalidate the model against current process knowledge.
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AI must not become a shortcut around established safety approvals, interlocks or emergency procedures. A recommendation that appears efficient can still be unsafe if it suppresses an alarm, changes a set point outside an approved range or encourages a worker to skip a required check. Safety functions should remain independently enforceable, and the organization should define which actions AI can suggest, which a human may authorize and which are prohibited.
Availability and physical consequence
An unavailable AI service can interrupt a workflow even when no attacker is present. If an operator cannot continue safely without a model or its data connection, the deployment has created a single point of failure. A safe fallback can be a manual procedure, an established non-AI control path or another previously validated operating mode. The fallback must be usable under pressure, not merely documented.
The AI attack surface operators must assess
NIST says AI systems bring familiar confidentiality, integrity and availability concerns across the model, its training and output data, the software and hardware that run it, and the systems connected to it. AI also introduces specialized threats that existing frameworks do not yet comprehensively address.
- Confidentiality: Sensitive operational data, model parameters or prompts may be exposed to an unauthorized party.
- Integrity: Altered training data, manipulated inputs or tampered model files can produce incorrect recommendations while appearing normal.
- Availability: An attack or outage can prevent the model, its data pipeline or a required interface from working when a service is needed.
- Evasion: An attacker can craft inputs intended to make a model misclassify an event or overlook malicious activity.
- Model extraction: Repeated queries or access to artifacts may reveal a model or enough of its behavior to reproduce it.
- Membership inference: An attacker may try to determine whether particular information appeared in training data.
- Complex dependencies: The attack surface includes data collection, update processes, third-party components, connected IT and OT networks, and the physical devices that execute commands.
These categories are not a claim that every deployment suffers every attack. They are prompts for a system-specific assessment. A model isolated to an analyst workstation has a different consequence profile from one that can influence a live process.
A practical risk-assessment method
Use the following as an operator’s working sequence, not as a universal regulatory checklist. Each answer should be recorded with an owner and a review date.
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- Define the service and boundary. Name the essential service, the process it depends on and the exact AI capability. Identify whether the model only displays information, recommends an action or can directly influence an OT control.
- Map dependencies. Include the model, training and live data, software, hardware, identity and access controls, connected IT and OT assets, communications links and safety processes. Record what happens if each dependency is inaccurate, compromised or unavailable.
- List credible events. Consider malicious inputs, stolen credentials, poisoned or stale data, model tampering, extraction, evasion, loss of connectivity, software failure and process-model drift. Include accidental operator misuse as well as deliberate attacks.
- Estimate likelihood and impact together. GAO identifies both the probability of an event and the magnitude of its harm as essential to evaluating risk. Consider cyber effects, service interruption, financial loss, environmental consequences and physical safety, rather than scoring likelihood alone.
- Assign a mitigation to each material risk. State which control addresses which event, who operates it and how its effectiveness will be checked. If no mitigation is practical, reconsider the AI capability or limit its authority.
- Set human accountability. Specify who reviews recommendations, who may override them and who can place the AI function in a safe state. Make escalation rules clear for ambiguous or high-consequence outputs.
- Prove a fallback. Exercise the non-AI procedure during an outage, bad-data condition and suspected compromise. Measure whether staff can preserve essential service without relying on the unavailable model.
- Reassess after change. Revisit the analysis when the model, data source, connected asset, operating range or threat environment changes. Drift monitoring and periodic review are necessary because a previously acceptable risk can change without a new product purchase.
Choosing the level of AI influence
There is no substantiated basis for ranking a particular vendor or architecture from the available evidence. Operators can, however, compare deployment patterns against the same decision axes: cyber-risk reduction, added attack surface, operational benefit, safety consequence, likelihood, impact, mitigation strength, monitoring, accountability and fallback.
| Deployment pattern | Potential value | Primary added exposure | Prudent control question |
|---|---|---|---|
| Analyst-only information and search | Faster review and better access to relevant records | Confidentiality, inaccurate summaries and overreliance | Can an analyst verify the source data and continue if the tool is unavailable? |
| Alert triage or recommendation | Decision support for security or operations staff | Evasion, stale data and misplaced confidence in a ranking | Is every consequential recommendation reviewable before action? |
| Workflow automation with approval | Efficiency and consistent execution of bounded tasks | Compromised credentials, bad automation logic and process drift | Which actions require explicit human authorization and independent safety checks? |
| Direct influence over OT | Potentially faster response to defined conditions | Physical harm, service interruption and safety-process bypass | Can the function be isolated, overridden and safely replaced by a validated manual mode? |
The higher the AI’s authority over a physical process, the stronger the evidence, testing, segregation and fallback should be before production use. A recommendation system is not risk-free, but it generally offers a smaller blast radius than an unreviewed control action.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Standards and policy: useful direction, unfinished work
NIST’s AI Risk Management Framework identifies “Secure and Resilient” as a primary characteristic of trustworthy AI. NIST is developing a Trustworthy AI in Critical Infrastructure profile intended to help operators identify risk-management practices and communicate requirements across AI and infrastructure lifecycles and supply chains. The NIST AI RMF page lists a concept note for that profile released April 7, 2026. It is a work in development, not a completed sector standard.
CISA’s 2023–2024 AI roadmap included assessing cybersecurity risks of AI adoption in critical infrastructure, including critical failures, physical attacks and cyberattacks. That stated period has passed, so the roadmap should be read as policy background rather than a current implementation schedule. CISA announced the JCDC AI Cybersecurity Collaboration Playbook and fact sheet on January 14, 2025, describing them as tools for operational collaboration among government, industry and international partners. Their existence and stated purpose do not, by themselves, prove an ongoing operating status for every organization.
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What GAO found about sector assessments
GAO’s December 18, 2024 report reviewed 16 sector assessments and one subsector assessment. None of the reviewed assessments fully evaluated risk by combining the likelihood of an event with the magnitude of its impact. GAO also reported that no agencies fully mapped mitigations to risks because the level of risk had not been evaluated. DHS agreed to update its guidance.
GAO’s recommendation page says the sector-specific assessment work was paused pending a federal policy review and gave March 31, 2027 as the estimated completion date, as of July 2026. That status is time-sensitive. An operator should verify any later federal update before treating the pause or target date as current policy. The practical lesson remains stable: a list of threats without likelihood, impact and linked mitigations is not a complete risk assessment.
Questions to settle before deployment
- What essential service could be interrupted if the model is wrong, unavailable or manipulated?
- Which data sources are authoritative, how fresh are they and who can alter them?
- Can the operator see why a recommendation was made and challenge it quickly?
- What technical and procedural barriers prevent AI from bypassing safety processes?
- What tests cover evasion, extraction, membership inference, availability failure and data or process drift?
- Who owns the decision to suspend the AI capability, and how is that decision communicated during an incident?
- What evidence will show that the mitigation still works after a model update or OT change?
Bottom line for infrastructure leaders
AI is best treated as a capability that can improve defensive analysis and operational decision support, not as an autonomous guarantee of security. The same technology can enlarge the attack surface, expose sensitive data, fail silently as conditions change or contribute to unsafe physical actions. Start with the service and its consequences, evaluate likelihood and impact together, connect every material risk to a mitigation, preserve human authority and rehearse a safe fallback. That discipline lets an operator capture useful AI assistance without allowing an experimental model to become an unexamined dependency in an essential service.
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