AI can improve detection, resilience, and operational decision-making in critical infrastructure, but it also adds new ways to fail and new targets for attackers. The safe approach is conditional: assess each use case by its possible impact and likelihood, limit the authority given to automated systems, validate data and models throughout their life, and design supervision so a qualified operator can detect, escalate, and override unsafe behavior. U.S. agencies describe substantial potential, while a 2024 Government Accountability Office (GAO) review found that federal sector assessments had not yet completed the basic risk picture needed to support broad assurances.
What AI cybersecurity means in critical infrastructure
Critical infrastructure includes the systems and services whose disruption can affect public safety, the economy, national security, or essential daily life. Energy, water, transportation, communications, health, and other sectors use a mixture of information technology (IT), operational technology (OT), industrial control systems (ICS), sensors, and manual procedures. An AI model connected to those environments is not just another software feature: an incorrect classification, delayed alert, or unsafe command can have physical consequences.
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In this setting, “AI cybersecurity” has two meanings that must be considered together:
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errors- Using AI to defend infrastructure: finding suspicious behavior, prioritizing alerts, forecasting equipment problems, supporting incident response, and helping operators understand complex conditions.
- Securing AI used in infrastructure: protecting its data, models, interfaces, software dependencies, and decisions from error, manipulation, theft, and misuse.
AI is therefore a dual-use capability. NIST says it may give defenders new tools while also improving the capabilities of attackers targeting both IT and OT. A deployment should be judged by the consequence of an incorrect or unavailable result, not by the novelty of the model.
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Where AI could improve security and resilience
The benefits described by government agencies are potential outcomes, not universal results demonstrated across every operator or sector. The Department of Energy (DOE) has focused specifically on energy infrastructure and says AI could benefit security, reliability, and resilience. Its assessment also describes work on tools addressing cyber and physical threats.
Faster detection and triage
AI can examine large volumes of network, endpoint, physical, and equipment data faster than a human team working manually. It may identify unusual combinations of events, group related alerts, and help analysts focus on the incidents most likely to matter. The useful measure is not the number of alerts processed; it is whether the system helps an accountable analyst make a better and faster decision without hiding important uncertainty.
Predictive maintenance and reliability support
Models can look for patterns associated with equipment degradation or abnormal operating conditions. In an energy environment, earlier indication of a problem could support planned maintenance and reduce the chance that a component fails during a stressed period. A prediction should remain advisory unless its accuracy, failure modes, and operating limits have been demonstrated for that particular asset and context.
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During an incident, AI may help correlate logs, map affected dependencies, suggest containment actions, or summarize changing conditions for an incident commander. These functions can reduce cognitive load, but the system must show the evidence behind a recommendation and distinguish observed facts from generated hypotheses.
Security analysis across physical and cyber data
Infrastructure operators often need to relate cyber events to physical processes. A suspicious login, a controller change, and a pressure or frequency anomaly may be separate records that become meaningful when viewed together. AI can assist with that correlation, while engineers remain responsible for determining whether the apparent relationship is real and safe to act upon.
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The four main ways AI can increase risk
DOE identifies four distinct risk pathways for AI in the energy sector. The same categories are useful for other infrastructure operators, but the consequences depend on the sector, asset, and level of automation.
1. Unintentional AI failures
A model may be wrong because its training data did not represent the operating environment, conditions changed after deployment, sensors failed, or the model encountered a case outside its design range. Drift can be gradual and difficult to notice. An automated recommendation that looks plausible can be more dangerous than an obvious error if operators accept it without checking.
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Attackers can target the model, its inputs, its interfaces, or the surrounding infrastructure. Manipulated sensor data may cause a false diagnosis; carefully crafted inputs may evade detection; stolen model information may reveal sensitive operational patterns. AI services also inherit ordinary security requirements for confidentiality, integrity, and availability.
3. Hostile use of AI
Adversaries can use AI to scale phishing, discover vulnerabilities, generate convincing impersonation, automate reconnaissance, or adapt attacks to defensive responses. The defensive system and the attacker may both become faster, increasing the value of strong authentication, network segmentation, and tested response procedures.
4. Compromised AI software supply chains
Risk can enter through a pretrained model, data set, library, container, update mechanism, contractor, or cloud service. A component may be tampered with before deployment or become vulnerable later. Operators need provenance records, integrity checks, dependency monitoring, and a way to remove or replace a component without losing control of the underlying process.
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AI-specific security problems NIST highlights
NIST notes that several AI risks overlap with conventional information-system security. Confidentiality protects sensitive data and models; integrity protects data, code, and outputs from unauthorized change; availability ensures that operators can obtain a service when needed. Secure hardware, software, identity management, logging, and network architecture remain necessary even when an AI component is added.
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NIST also identifies attack concerns that are more specific to machine-learning systems:
| Concern | What an attacker or failure may do | Operational question |
|---|---|---|
| Evasion | Craft an input that causes a detector or classifier to miss malicious activity or misclassify a condition. | How does the system behave when data are deliberately altered or are outside normal ranges? |
| Model extraction | Reconstruct a model or infer its behavior through repeated queries. | What information can an external or internal user learn from the interface? |
| Membership inference | Infer whether a particular record was included in training data. | Could outputs reveal sensitive information about people, facilities, or incidents? |
| Availability attacks | Exhaust resources, disrupt access, or otherwise prevent the model from serving operators. | What safe fallback exists if the AI service is unavailable? |
NIST cautions that existing frameworks do not comprehensively address the entire AI attack surface. Treating a model as a normal application without examining these additional behaviors leaves important gaps.
What federal assessments show about preparedness
GAO’s December 2024 review examined 16 sector assessments and one subsector assessment—17 in total. The assessments identified more than 150 beneficial AI use cases, a count reported by GAO from sector risk-management agencies. That number describes identified possibilities, not measured cybersecurity improvements or successful deployments.
None of the 17 assessments fully addressed all six activities GAO selected as necessary for 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 result is a basic governance problem: agencies had catalogued uses without consistently establishing how likely failures or attacks were, how severe their effects could be, or which controls would reduce the risk.
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GAO reported that the Department of Homeland Security and the Cybersecurity and Infrastructure Security Agency issued revised guidance and a template in August 2024, but gaps remained. On GAO’s recommendation page, the status updated 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 cannot proceed until that review concludes and gives March 31, 2027 as the estimated completion date for the recommendation. This is a status of the federal review process, not proof that every current operator has the same deficiencies.
A lifecycle governance method that fits infrastructure operations
NIST’s AI Risk Management Framework (AI RMF) playbook organizes suggested practices into four functions: Govern, Map, Measure, and Manage. The playbook is voluntary and based on AI RMF 1.0, released January 26, 2023. It is a governance aid, not a certification or a universal regulatory requirement.
Govern: assign authority and accountability
- Name the business owner, system owner, security authority, engineering authority, and operational approver for each AI capability.
- Define what the model may recommend, what it may execute, and what it may never do.
- Set documentation, access-control, logging, procurement, and retirement requirements before production use.
- Require a change process for model updates, data changes, new integrations, and shifts in operating conditions.
Map: describe the real operating context
- Record the asset, process, users, data sources, dependencies, affected populations, and geographic scope.
- Identify consequences for safety, service continuity, privacy, environmental protection, and national security.
- List plausible misuse, failure, and attack paths, including dependencies on vendors and cloud services.
Measure: test performance and failure behavior
- Validate data quality, provenance, representativeness, labeling, and sensor integrity.
- Test normal, rare, degraded, adversarial, and out-of-distribution conditions.
- Measure false positives, false negatives, latency, uncertainty, drift, and the effect of missing data.
- Test the complete human-and-machine workflow, not only the model in isolation.
Manage: reduce risk and monitor change
- Use layered controls such as segmentation, least privilege, independent checks, rate limits, and safe fallback modes.
- Track incidents, near misses, overrides, drift, supplier changes, and unexplained output changes.
- Set explicit thresholds for pausing the model, reverting to a known-good version, or switching to manual operation.
- Retire a capability when its risks exceed its value or its operating assumptions no longer hold.
How to design human oversight that works
GAO lists human monitoring of automated processes among the AI-specific mitigation methods discussed by agencies. Human oversight is not a slogan or a requirement that one person watch every screen continuously. The control must match the operational consequence and the speed of the process.
Match supervision to consequence and reversibility
A model that summarizes threat reports can usually be reviewed after the fact. A model that recommends opening a breaker, changing a treatment parameter, or isolating a network segment may require approval before execution. For actions that are difficult to reverse or could affect safety, use a qualified operator with the authority, information, and time to intervene.
Make the operator’s decision possible
Present the evidence used, the model’s uncertainty, the age and quality of inputs, and the consequences of each proposed action. Provide a clear escalation path when the operator cannot validate the recommendation. Avoid interfaces that imply certainty or make accepting the AI output easier than investigating it.
Prove that override and fallback work
Exercise manual procedures, independent controls, and safe-state behavior during drills. Verify that an operator can disable automation without losing visibility or control, and that the process remains safe if the model, network, identity service, or data feed becomes unavailable.
Choosing an appropriate deployment approach
There is no single “safe AI” architecture for every facility. Compare a proposed approach using the following operational axes rather than a generic accuracy claim.
| Decision axis | Lower-consequence pattern | Higher-consequence pattern |
|---|---|---|
| Operational consequence | Advisory analysis with time for review. | Actions that can affect safety, physical processes, or essential service. |
| Authority over OT or ICS | Read-only access or a segregated recommendation channel. | Direct write or control authority, requiring stronger approval and independent safeguards. |
| Data provenance | Documented, stable, quality-checked sources. | Heterogeneous, safety-critical, or vendor-controlled data requiring continuous validation. |
| Failure validation | Routine accuracy and drift testing may be adequate for the consequence. | Adversarial, degraded-mode, failover, and scenario testing are necessary before changes. |
| Human intervention | Periodic review with documented escalation. | Named, trained operators able to detect, stop, and override the process in time. |
Implementation steps for an operator
- Inventory the use case. Document the model, version, owner, data sources, interfaces, locations, users, and every action it can trigger.
- Classify consequence. Estimate the impact of a wrong, late, unavailable, or manipulated output on safety, service, security, privacy, and the environment.
- Estimate likelihood. Consider historical failures, exposure, attacker capability, data quality, model drift, and dependency outages. Record uncertainty instead of hiding it.
- Set authority boundaries. Start with read-only or advisory access where feasible. Add automation only after testing demonstrates that controls and fallback behavior are adequate.
- Validate data and model behavior. Test representative and edge cases, poisoned or manipulated inputs, missing sensors, degraded networks, and unexpected operating states.
- Build monitoring and logging. Capture inputs, outputs, confidence or uncertainty, operator actions, overrides, model changes, and supplier updates in a way investigators can use.
- Train and exercise people. Teach operators what the model can and cannot do, how to challenge it, and how to switch to manual procedures. Run realistic drills.
- Prepare incident response. Define who can isolate the AI component, revoke credentials, preserve evidence, notify affected parties, restore a known-good version, and communicate with regulators or partners.
- Review continuously. Reassess after incidents, near misses, major data changes, software updates, changes in threat activity, or changes to the physical process.
Supply-chain and workforce controls
AI risk management extends beyond the facility. Obtain an inventory of models, datasets, libraries, containers, APIs, and hosting providers. Require version records, integrity verification, vulnerability handling, access restrictions, and notice of material supplier changes. Keep an option to operate safely if a provider is unavailable or a component must be withdrawn.
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Where U.S. guidance stands now
NIST’s project for a Trustworthy AI in Critical Infrastructure profile is intended to provide practices for AI-enabled capabilities and communicate trustworthiness requirements across AI and infrastructure lifecycles and supply chains. The project began in April 2026 and was listed as ongoing on the page updated July 17, 2026. It is not finalized guidance, so operators should not present it as a completed standard.
The practical position is therefore conditional rather than binary. AI may strengthen critical-infrastructure cybersecurity when its use case is bounded, its data and behavior are tested, its dependencies are controlled, and people can intervene. It can increase danger when automation outruns validation, when risk assessments omit likelihood and impact, or when operators cannot see or override what the system is doing.
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