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AI can improve the security, reliability, and resilience of critical infrastructure, but it also introduces new ways for systems to fail and for attackers to cause harm. The safest approach is conditional: use AI where its outputs can be validated, constrained, monitored, and overridden by qualified operators. Treat every deployment as a lifecycle risk-management problem rather than as an automatic upgrade to cybersecurity.
This article focuses on the U.S. federal evidence available through July 2026. Department of Energy (DOE) findings are specific to the energy sector; Government Accountability Office (GAO), NIST, and CISA material covers broader federal critical-infrastructure policy and assessment.
What AI cybersecurity means in critical infrastructure
Critical infrastructure includes the systems and facilities that support energy, water, transportation, communications, health, finance, emergency services, and other essential functions. These environments combine information technology (IT) with operational technology (OT), industrial control systems (ICS), sensors, physical equipment, and processes where a bad decision can affect safety or availability.
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AI cybersecurity is therefore more than putting a machine-learning detector in a security operations center. It includes securing the AI system itself, securing the data and software it depends on, deciding what authority it has over IT or OT, and ensuring that people can recognize and correct an unsafe result. A model that is accurate in a test environment can still be inappropriate for a process that cannot tolerate a delayed alarm, an unexplained action, or a false shutdown.
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Federal agencies describe capabilities and risks, not a universal performance guarantee. No comparable federal statistic establishes that AI deployments have improved cybersecurity across critical infrastructure as a whole.
Where AI could help
Security monitoring and threat analysis
AI can potentially examine large volumes of logs, network events, equipment telemetry, and physical-security signals faster than a team working manually. It may help identify unusual relationships, prioritize alerts, summarize incidents, or find indicators that deserve human investigation. In an OT environment, those uses should remain bounded by the consequences of acting on an incorrect classification.
Reliability and resilience
DOE says AI could benefit energy infrastructure across security, reliability, and resilience. Possible applications include forecasting demand or equipment conditions, helping operators plan maintenance, and supporting recovery decisions during disruptions. These are potential outcomes described by DOE, not measured results that can be generalized to every utility or infrastructure operator.
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DOE also describes work on AI tools for security and resilience against cyber and physical threats. A system that correlates a cyber event with a physical change could help an operations team investigate faster. The usefulness depends on trustworthy sensor data, clear operating limits, and a response process that does not let a probabilistic output trigger an unsafe action without appropriate checks.
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Supporting people under pressure
During a major incident, AI may help organize alerts, retrieve procedures, translate technical information, or draft a timeline for responders. The model should support a named decision-maker rather than silently becoming one. Every recommendation needs a visible source, confidence or uncertainty information where meaningful, and a way to reject it.
The four main risk pathways
DOE identifies four distinct ways AI can create risk in critical infrastructure. Separating them prevents an organization from treating “AI risk” as one generic problem.
| Risk pathway | What it means | Control questions |
|---|---|---|
| Unintentional AI failure modes | A model, integration, or automated workflow behaves incorrectly without an attacker directly manipulating it. | What happens when data is missing, stale, out of range, or unlike the training examples? Is the safe state defined? |
| Adversarial attacks against AI | An attacker deliberately manipulates inputs, models, or interfaces to evade detection, extract information, deny service, or induce a wrong result. | Can inputs be poisoned or crafted to fool the model? Can a defender detect tampering and fall back safely? |
| Hostile applications of AI | An adversary uses AI to improve phishing, discovery, deception, malware development, targeting, or attacks on IT and OT. | How will the organization detect faster, more adaptive attacks and protect sensitive operational information? |
| Compromised AI software supply chain | A model, library, data set, update, cloud service, or development component is altered or delivered with hidden weaknesses. | Who supplied each component? Can versions be verified, updated, rolled back, and investigated? |
These pathways can overlap. For example, a compromised update can create an unintentional failure that an attacker later exploits. A risk register should record the initiating event, affected assets, operational consequence, detection method, and recovery action separately.
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AI-specific security issues NIST highlights
NIST distinguishes ordinary system-security requirements from AI-specific attack surfaces. Confidentiality, integrity, and availability still apply to systems and data, as do secure hardware, software development, identity management, patching, and access control. Adding a model does not remove those obligations.
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NIST also identifies AI attack concerns including:
- Evasion: inputs are modified so a model misses a malicious or abnormal condition.
- Model extraction: an attacker infers or copies model behavior through repeated queries.
- Membership inference: an attacker attempts to determine whether particular data was used to train a model, which can expose sensitive information.
- Availability attacks: an attacker prevents the model or its supporting service from producing timely results.
NIST warns that current frameworks do not comprehensively address the entire AI attack surface. AI is dual-use: it may give defenders new analytical tools while also increasing the capabilities of attackers targeting IT and OT. Security testing must therefore include both failure of the model and abuse of the model.
What federal assessments found
GAO’s December 2024 review examined 16 sector assessments and one subsector assessment, for 17 assessments in total. The agencies identified more than 150 beneficial AI use cases. That number is a count of identified use cases, not evidence that the uses delivered measurable cybersecurity benefits.
None of the 17 assessments fully addressed all six selected activities GAO used to evaluate effective risk assessment and mitigation. Most did not fully identify potential risks, including their likelihood, and none fully evaluated risk level as a combination of impact and likelihood. The findings describe the federal assessments and guidance period; they do not prove that every current infrastructure operator has the same gap.
DHS and CISA issued revised guidance and a template in August 2024, but GAO reported that deficiencies remained. On GAO’s recommendation page, the status current through July 2026 says DHS paused implementation of National Security Memorandum 22 during a structured review of federal preparedness and infrastructure policy. GAO says sector-specific assessments could not proceed until that review concludes and lists March 31, 2027 as the estimated completion date for the recommendation. This is a reported status as of July 2026, not a prediction that the date will be met.
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What the numbers do—and do not—show
| Figure | Meaning | Limitation |
|---|---|---|
| More than 150 | Beneficial AI use cases identified by sector risk-management agencies, as reported by GAO in 2024. | Not a measure of successful deployment or security improvement. |
| 17 | Sector and subsector assessments GAO reviewed. | A federal assessment sample, not every operator or facility. |
| None of 17 | Fully addressed all six selected assessment activities. | Shows assessment gaps; it does not establish that every AI system is unsafe. |
Why human oversight is a security control
GAO lists human monitoring of automated processes among the AI-specific mitigation strategies discussed by agencies. In practice, oversight means more than putting a person somewhere in an approval chain. An operator must have enough context, time, authority, and technical access to detect an abnormal recommendation, escalate it, and intervene.
Match supervision to operational consequence
A model that summarizes low-impact tickets can be reviewed after the fact. A model that recommends an equipment set-point, blocks a connection, isolates a network segment, or initiates a restart needs tighter controls. The more consequential, irreversible, or safety-sensitive the action, the stronger the requirements for pre-action validation, independent checks, explicit authorization, and a tested manual fallback.
Make intervention practical
- Show the input data, time stamp, model version, and reason codes that support an alert or recommendation.
- Define who can approve, reject, pause, or override the automated action.
- Provide a safe mode that preserves essential service when the model, data feed, or communications link fails.
- Log decisions and overrides so investigators can reconstruct what the system and operator knew at the time.
- Exercise the procedure with realistic degraded-data and adversarial scenarios.
There is no universal human-in-the-loop standard for every infrastructure use case in the reviewed material. Oversight design must follow the process’s hazard, speed, reversibility, and staffing reality.
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NIST’s voluntary AI Risk Management Framework (AI RMF) playbook organizes suggested practices into four functions. AI RMF 1.0 was released on January 26, 2023. The playbook is a governance aid, not a certification and not a mandatory regulation.
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| Function | Critical-infrastructure questions |
|---|---|
| Govern | Who owns the system, approves its use, manages suppliers, sets accountability, and funds monitoring and incident response? |
| Map | What process, assets, people, data, hazards, dependencies, and affected communities are in scope? What could happen if the model is wrong? |
| Measure | How will accuracy, robustness, security, privacy, drift, bias, latency, and failure behavior be tested under normal and adversarial conditions? |
| Manage | Which risks are accepted, reduced, transferred, or avoided? What triggers suspension, rollback, retraining, or retirement? |
NIST’s Trustworthy AI in Critical Infrastructure profile is intended to help operators apply practices to AI-enabled capabilities and communicate trustworthiness requirements across AI and infrastructure lifecycles and supply chains. The project began in April 2026 and was still ongoing on the page updated July 17, 2026. It should not be presented as finalized guidance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate an AI deployment before it reaches operations
- Define the decision and authority. Write down whether the system observes, recommends, approves, or executes. Identify every IT, OT, ICS, physical, and safety function it can influence.
- Describe the consequence of error. Estimate impact and likelihood for false positives, false negatives, delayed output, corrupted data, unavailable service, and unsafe automation. Do not record impact without likelihood; GAO identified that omission in federal assessments.
- Validate data provenance. Document collection methods, labels, time periods, operating conditions, missing values, permissions, and retention. Test whether data represents seasonal, emergency, maintenance, and degraded-network conditions.
- Test behavior, not just accuracy. Include distribution shifts, noisy sensors, adversarial inputs, prompt or interface abuse where relevant, model extraction attempts, privacy attacks, and service outages. Measure whether the system fails safely.
- Constrain actions. Use least privilege, allow-lists, rate limits, approval gates, network segmentation, and independent interlocks. Keep read-only or advisory operation when execution cannot be adequately validated.
- Design the operator workflow. Assign named roles for review, escalation, override, and incident command. Ensure alerts arrive early enough for a human to act and that the fallback procedure works without the AI service.
- Control the supply chain. Inventory models, data sets, libraries, hardware, APIs, and hosting providers. Verify provenance and integrity, review update permissions, preserve known-good versions, and rehearse rollback.
- Monitor continuously. Track drift, false-alarm rates, latency, unexplained behavior, access logs, data-quality changes, and operator overrides. Set thresholds that trigger investigation or suspension.
- Prepare incident response. Add AI-specific scenarios to existing plans: poisoned data, evasion, model theft, unavailable inference, unsafe recommendation, compromised update, and loss of human visibility. Preserve evidence needed to identify the model and data version involved.
- Reapprove after change. A retrained model, new data source, changed threshold, supplier update, or expanded authority can alter risk. Reassess before returning it to production.
Choosing between deployment approaches
There is no official ranking of “best” AI architecture. Operators can compare approaches using the following axes, which follow from the federal risk and oversight evidence.
| Comparison axis | Lower-risk characteristics | Questions that can change the decision |
|---|---|---|
| Operational consequence | Advisory output with reversible effects. | Could a wrong output interrupt service, damage equipment, or endanger people? |
| Authority over OT or ICS | No direct write access; independent controls remain authoritative. | What approvals and interlocks are required before any command? |
| Data quality and provenance | Documented, representative, integrity-checked data with known gaps. | How does performance change during emergencies, maintenance, sensor failure, or novel conditions? |
| Validation and recovery | Repeatable tests, adversarial evaluation, rollback, and a manual fallback. | Can the team detect a bad result and restore a known-good state quickly? |
| Human detectability and override | Visible rationale, actionable alerts, trained staff, and tested intervention. | Will a person have the authority and time to stop the action? |
A less autonomous design may be the appropriate choice when consequences are severe, data is sparse, or behavior cannot be validated. Greater automation is defensible only when the organization can demonstrate bounded authority, reliable monitoring, and recovery under realistic failure conditions.
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Technology controls cannot compensate for an organization that has no people able to interpret alerts or operate the fallback. Workforce development should cover OT and ICS processes, AI limitations, secure data handling, adversarial behavior, incident command, and the authority to stop an unsafe automated action. Cross-functional review should include cybersecurity, engineering, safety, legal or privacy specialists where relevant, procurement, and the people who operate the process around the clock.
Document staffing assumptions. A design that requires continuous expert review is not resilient if the facility has one specialist on call for several sites. Conversely, removing review to solve a staffing problem can convert an uncertain recommendation into an unobserved control action.
Can AI be trusted to protect critical infrastructure?
AI can be trusted only for a defined purpose, within demonstrated limits, with controls proportionate to the operational consequence. Trust is not a permanent property of a model: it can change when data drifts, software is updated, an attacker adapts, or the system is connected to a new process.
The practical standard is therefore conditional assurance. Before deployment, show what the system is allowed to do and how it was tested. During operation, monitor data, behavior, security, and operator intervention. After an incident or material change, reassess rather than assuming the prior approval still applies. This approach captures AI’s potential benefits without treating agency descriptions of capability as proof of universal effectiveness.
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