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AI adoption creates a two-sided cybersecurity problem for critical infrastructure. Artificial intelligence can help operators detect anomalies, prioritize maintenance and find vulnerabilities, but it can also give attackers better capabilities and introduce new weaknesses into the systems that run power, water, transport, health and other essential services. When AI is connected to operational technology (OT), a bad decision can affect physical safety, service availability and process reliability—not just data confidentiality.
The practical answer is disciplined, continuous risk management: inventory every AI dependency, assess impact and likelihood together, preserve human and engineering controls, test models under realistic operating conditions, and maintain enough OT expertise to respond when systems or assumptions change.
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What are the cybersecurity challenges of AI adoption in critical infrastructure?
The central challenge is that “AI adoption” covers two materially different activities:
- AI embedded in infrastructure operations: a model may monitor sensors, recommend set points, predict equipment failures or automate a control-room decision.
- AI used for cyber defense: security teams may use models to identify suspicious behavior, prioritize vulnerabilities or help remediate weaknesses.
These uses have different failure modes, but they can coexist in the same environment. A defensive model can expose sensitive operational data, while an operational model can change a physical process. Treating both as a generic software deployment misses the safety, reliability and availability requirements of OT.
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NIST describes the problem as evolving rapidly: “The security and resilience of AI technologies is an area of active research, and challenges and potential solutions are changing very rapidly.” Its work identifies risks to AI systems, their data, software and hardware, as well as the possibility that AI will improve both defensive and offensive cyber capabilities. See NIST’s AI security and resilience research.
How AI affects operational technology security
OT monitors or controls real-world processes. In a conventional information-technology application, an incorrect output might corrupt a record or interrupt a business workflow. In OT, the same kind of error can cause an unsafe sequence, a production shutdown or loss of a critical service. Latency, deterministic behavior, fail-safe states and clear operator authority therefore matter alongside conventional security objectives.
Joint guidance published by CISA, the Australian Signals Directorate’s Australian Cyber Security Centre and partner agencies warns that integrating AI into OT “introduces significant risks—such as OT process models drifting over time or safety-process bypasses.” The guidance says owners and operators must manage those risks to preserve availability and reliability. Read the full Principles for the Secure Integration of Artificial Intelligence in Operational Technology.
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An AI model learns relationships among equipment, sensors and operating conditions. Those relationships can change when a plant is modified, a sensor degrades, a fuel mix changes, weather shifts or operators adopt a new procedure. A model that was accurate during commissioning can become unsafe without any malicious code change. Monitoring must therefore detect changes in data and process behavior, not merely verify that the model is still running.
Safety-process bypasses
Automation can accidentally route around interlocks, approval gates or established shutdown procedures. A recommendation engine should not silently become the authority that controls a safety-critical action. Operators need an explicit decision boundary: which actions are advisory, which require confirmation, and which are technically impossible for the model to execute without independent safeguards.
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Availability and reliability consequences
OT often prioritizes continuous, predictable service. Updating a model, changing a data feed or isolating a compromised component can affect production. Security controls must be designed with maintenance windows, manual fallback, redundancy and recovery time in mind. Confidentiality remains important, but an availability or integrity failure can have immediate physical consequences.
Operational AI and defensive AI: a risk comparison
The following distinction helps an organization assign the right controls before it approves a use case.
| AI use | What it may do | Primary consequence if wrong or compromised | Risk questions |
|---|---|---|---|
| AI embedded in OT operations | Monitor conditions, predict failures, recommend or automate process decisions | Unsafe operation, service interruption, process instability or loss of reliability | Can it affect a control loop? What human or engineering interlock remains? How is drift detected? |
| AI supporting cyber defense | Detect anomalies, prioritize vulnerabilities, investigate alerts or assist remediation | Missed attack, false positive, exposure of sensitive OT data or an unsafe remediation | What data leaves the environment? Who approves changes? Can a model-generated action be reversed? |
The categories are not mutually exclusive. A security model may ingest process data, and an operational model may be connected to enterprise networks or vendor services. Map the complete data and control path rather than assessing a model in isolation.
AI-specific attack surfaces operators must account for
NIST identifies confidentiality, integrity and availability risks affecting the AI system, training data, output data, underlying software and hardware. Existing guidance does not comprehensively cover every machine-learning attack class or the full complexity of the attack surface. Relevant attack patterns include:
- Evasion: crafted inputs cause a model to misclassify a condition or overlook malicious activity.
- Model extraction: an attacker reconstructs a model through repeated queries, potentially revealing proprietary behavior or making later attacks easier.
- Membership inference: outputs reveal whether particular records were used in training, creating privacy or intelligence risks.
- Availability attacks: resource exhaustion, poisoned inputs or service disruption prevent the model from producing timely results.
- Data and pipeline compromise: altered training, calibration or sensor data changes what the model learns or sees.
- Component compromise: vulnerable libraries, firmware, accelerators, APIs or remote-management paths undermine an otherwise well-designed model.
These are in addition to ordinary access-control, segmentation, patching, identity, backup and incident-response requirements. “AI security” is not a substitute for basic cyber hygiene; it adds another layer of dependencies to secure and monitor.
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What oversight findings say about readiness
Federal reviews identify weaknesses in both assessment quality and organizational capacity. They are findings about the materials and entities GAO examined, not a statistical estimate of every infrastructure operator.
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In GAO-25-107435, published in 2025, the Government Accountability Office reviewed 16 sector assessments and one subsector assessment. None fully addressed the six activities GAO considered foundational to effective risk assessment and mitigation. Most agencies identified AI use cases but did not fully identify risks together with their likelihood, and none fully evaluated risk using both impact and likelihood. A revised assessment template closed some gaps but not all of them.
The implication is practical: listing an AI project is not the same as demonstrating that its risks are understood. An assessment that describes possible harms without estimating likelihood cannot reliably prioritize controls, funding or testing.
OT workforce and coordination
In GAO-24-106576, published March 7, 2024, GAO examined CISA services and selected agencies and nonfederal entities. Twelve of 13 selected nonfederal entities reported positive experiences with CISA OT products and services; seven also identified negative experiences as a challenge. The sample is not an industry-wide rate. The review identified issues involving service experience, staffing, information sharing and interagency coordination.
CISA officials told GAO that four federal employees and five contractor staff were insufficient to respond to significant attacks affecting OT systems in multiple locations at once. That is a dated staffing example from the report, not a current workforce count. GAO recommended measuring customer service, planning the OT workforce and improving collaboration processes. Operators should read the finding as a reminder that tools cannot compensate for unclear responsibility or inadequate surge capacity.
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How should critical infrastructure operators assess AI risk?
Use a lifecycle assessment that connects each material risk to an owner, a mitigation and a test. The sequence below is a practical baseline; sector regulators and safety engineers may require additional controls.
- Inventory the use case. Record every model, vendor component, API, data source, hardware accelerator and software dependency. Identify whether the system is advisory, supervisory or able to issue commands.
- Map the operational boundary. Document which OT assets, control loops, safety systems and business networks can influence the AI or be influenced by it. Include remote access, update paths and cloud or supplier dependencies.
- Classify data and outputs. Identify training, calibration, telemetry and output data. Mark sensitive process information, credentials and personal data, and define where each item may be stored or transmitted.
- Assess impact and likelihood together. For each failure, misuse or attack, estimate consequences for safety, service availability, reliability, integrity and confidentiality, then estimate how likely the scenario is under credible conditions. GAO’s review found this combined evaluation missing in the sector materials it examined.
- Define mitigations before approval. Assign controls such as network segmentation, least-privilege access, signed artifacts, data validation, rate limits, independent safety interlocks, manual fallback and tested recovery. State who owns each control.
- Test under realistic conditions. Test normal operations, degraded sensors, missing data, adversarial inputs, model drift, delayed responses and loss of connectivity. Include operators and safety personnel, not only data scientists.
- Set change and retirement triggers. Specify when retraining, revalidation, rollback or shutdown is required. A model should have a documented version, an approved operating range and a way to return to a known-good state.
Governance safeguards that make adoption safer
Keep human and engineering authority explicit
Human review is meaningful only when the person has time, information and authority to reject an output. Display confidence, relevant context and the reason for an alert where feasible. Independent interlocks, permissive logic and emergency shutdowns should remain outside the model’s control path when safety requires independence.
Protect the data and model supply chain
Control access to training and output data, verify provenance, monitor unexpected changes and maintain an inventory of third-party components. Secure update channels and retain prior model versions so a compromised or degraded release can be withdrawn. Supplier contracts should define notification, logging, support and recovery responsibilities.
Monitor for drift and abuse
Track input distributions, sensor quality, output ranges, latency, override rates and anomalous query behavior. Establish thresholds that trigger investigation rather than allowing silent degradation. Logging must support both cyber forensics and operational troubleshooting without becoming an uncontrolled repository of sensitive process data.
Exercise failure and recovery
Tabletop and technical exercises should cover model outage, poisoned data, credential theft, ransomware affecting an AI dependency and a false recommendation during an abnormal process condition. Validate that operators can continue safely in manual or degraded modes and that restoration does not reintroduce a compromised model.
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Workforce and coordination are security controls
AI projects require people who understand both machine learning and the process being protected. A data scientist may recognize statistical drift but miss the operational meaning of a valve state; an OT engineer may understand the process but need support to evaluate model extraction or pipeline compromise. Define joint ownership among control-room operators, OT engineering, cybersecurity, safety, procurement, legal and vendors.
Maintain contact paths for incident escalation, threat intelligence and external assistance. Share the minimum information needed with government and sector partners while protecting sensitive operational details. Staff planning should include vacations, maintenance windows, simultaneous incidents and supplier unavailability, not only normal staffing levels.
What federal policy adds—and what it does not prove
Executive Order 14110 directed agencies with relevant authority and sector-risk roles to assess AI risks to critical infrastructure at least annually, including potential critical failures, physical attacks and cyberattacks, and to consider mitigations. It also described federal pilots to identify, develop, test, evaluate and deploy AI capabilities that could help discover and remediate vulnerabilities in federal systems and networks. The order’s text is available from the U.S. Government Publishing Office.
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Those provisions establish policy direction; they do not demonstrate that every sector has completed an assessment or that a pilot is deployed everywhere. Confirm current policy status, agency implementation and sector-specific obligations before treating the order as a present compliance checklist.
Where standards and guidance are heading
NIST is developing an AI Risk Management Framework profile for trustworthy AI in critical infrastructure. NIST describes the profile as work in development intended to guide risk-management practices across AI and critical-infrastructure lifecycles and supply chains. Operators should use available NIST and sector guidance while recognizing that a finalized critical-infrastructure profile is not yet identified on the cited page.
The 2025 joint OT guidance, NIST’s security research and applicable safety and sector regulations should be read together. None replaces site-specific hazard analysis, engineering standards, incident-response plans or contractual requirements.
A decision test for approving an AI deployment
Before connecting a model to an essential process, an approval board should be able to answer “yes” to each question:
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- Is the model’s purpose, authority and operating boundary documented?
- Are safety, reliability, availability, integrity and confidentiality impacts assessed separately?
- Are likelihood and impact evaluated together, with a named mitigation for each material risk?
- Can the process remain safe if the model, its data feed or its network connection fails?
- Are drift, adversarial inputs, supply-chain changes and unauthorized access monitored?
- Have operators, safety staff and incident responders tested realistic failure scenarios?
- Is there a tested rollback, manual mode and retirement decision?
- Are responsibilities and escalation contacts clear across the operator, supplier and government partners?
If any answer is no, delay expansion or limit the system to a segregated, non-controlling pilot until the gap is addressed. AI can strengthen critical-infrastructure defense, but only when its added capability is matched by stronger governance, testing and operational safeguards.
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