Quick wins for a faster PC:
Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →iTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
AI adoption creates a two-sided cybersecurity problem for critical infrastructure. The technology can improve monitoring, analysis, and vulnerability response, but it also expands the attack surface and can influence operational-technology (OT) decisions whose failure affects safety, reliability, and public-service availability. Effective programs separate AI used to run infrastructure from AI used to defend it, then apply documented risk assessment, testing, human oversight, and workforce planning to both.
What are the cybersecurity challenges of AI adoption in critical infrastructure?
Critical-infrastructure operators are introducing AI for forecasting, anomaly detection, maintenance, control-room decision support, and workflow automation. At the same time, security teams are testing AI to find vulnerabilities, triage alerts, and accelerate remediation. Those uses have different failure modes, yet they are often discussed as if “AI risk” were one problem.
AI connected to OT can affect physical processes, not just information. A bad recommendation, corrupted model, unavailable service, or unnoticed change in a process model can reduce the safety or reliability of electricity, water, transport, healthcare, communications, or other essential services. AI used for cyber defense normally does not directly control a process, but it can still misclassify an attack, expose sensitive data, or automate an unsafe change.
A 2025 joint guide from CISA and international partners puts the operational consequence plainly: “Despite the many benefits, integrating AI into operational technology (OT) environments that manage essential public services also introduces significant risks—such as OT process models drifting over time or safety-process bypasses—that owners and operators must carefully manage to ensure the availability and reliability of critical infrastructure.” Read the joint OT guidance.
#1 Best Overall
AI used in operations versus AI used for cyber defense
The first governance decision is to identify what the model is allowed to do. An analytic model that recommends an inspection interval is not governed like an agent that can write to a controller. Conversely, a defensive model may not touch OT directly, but its output can trigger isolation, patching, or account changes that interrupt service.
| Dimension | AI embedded in OT operations | AI supporting cyber defense |
|---|---|---|
| Primary purpose | Monitor or optimize a physical process, predict conditions, or support operator decisions. | Detect threats, prioritize vulnerabilities, investigate events, or help develop remediation. |
| Immediate consequence of error | Unsafe operating conditions, process instability, equipment damage, or loss of service availability and reliability. | Missed or false alerts, delayed response, exposure of security data, or an incorrect automated change. |
| Important assets to protect | Models, sensor and training data, control interfaces, engineering workstations, network connections, and the underlying software and hardware. | Security telemetry, incident data, model prompts and outputs, orchestration tools, credentials, and remediation pipelines. |
| Minimum human control | Defined operating limits, independent safety controls, manual fallback, and an accountable operator for consequential actions. | Approval gates for disruptive actions, explainable escalation, rollback, and review of model-generated findings. |
| Evidence needed before expansion | Testing under normal, degraded, and abnormal process conditions, with safety and reliability acceptance criteria. | Measured detection quality, adversarial testing, data-protection review, and proof that automated changes can be reversed. |
Executive Order 14110 described both sides of this distinction: it directed relevant agencies to assess AI risks to critical infrastructure and also called for pilots using AI to discover and remediate vulnerabilities in federal systems and networks. The order is a policy provision, not evidence that every pilot or mitigation has been implemented; current policy status should be checked before treating it as an active requirement. See the official text of Executive Order 14110.
How AI changes the OT threat model
Process-model drift
Many operational models learn expected relationships among sensor readings, equipment states, and production conditions. Those relationships can change as equipment ages, a configuration is altered, a seasonal pattern shifts, or data quality degrades. If the model continues to treat old behavior as normal, it may recommend an unsafe action or suppress a warning. Drift therefore needs a defined detection threshold, a retraining or retirement process, and an operator who can reject the output.
Safety-process bypasses
Integrating an AI recommendation into a control workflow can unintentionally skip an existing interlock, approval, or maintenance check. A secure design keeps safety functions independent of the model, restricts the model’s authority to the minimum necessary, and records every proposed and accepted action. The joint guidance identifies bypasses as a risk to manage, not as a claim that every deployment has suffered one.
Expanded connectivity and privilege
An AI service may need data from historians, supervisory systems, enterprise applications, cloud APIs, or a vendor support channel. Each connection can become a path into a sensitive environment. Inventory the data flows and identities, segment networks, use least-privilege credentials, and make an explicit decision about whether the model can send commands or only recommendations.
Availability and timing
OT often has strict timing and uptime requirements. A model that is accurate but slow, unavailable during a network partition, or dependent on a remote service can be operationally unsuitable. Define a safe behavior for missing data, stale outputs, model outage, and loss of the external connection. Manual procedures and deterministic control logic should remain available.
AI-specific attack surfaces beyond ordinary cybersecurity
NIST describes AI security as an active area of research and warns that existing guidance does not comprehensively cover the complex attack surface. Its risk framing includes the confidentiality, integrity, and availability of AI systems; the protection of training and output data; and weaknesses in the software and hardware on which models depend. It also identifies attack classes such as evasion, model extraction, membership inference, and availability attacks. NIST’s security and resilience research page states that “the security and resilience of AI technologies is an area of active research, and challenges and potential solutions are changing very rapidly.”
- Data poisoning or manipulation: altered training, calibration, or live sensor data can teach a model the wrong baseline or hide an abnormal condition.
- Evasion: an attacker crafts inputs that make a detector or classifier miss malicious activity or accept an unsafe state.
- Model extraction: repeated queries can reveal a proprietary model or help an attacker reproduce its behavior.
- Membership inference: model responses may reveal whether sensitive records were included in training.
- Availability attacks: resource exhaustion, malformed inputs, or dependency outages can make an AI service unavailable when operators need it.
- Software and hardware compromise: libraries, model files, accelerators, firmware, and update mechanisms add supply-chain dependencies that require provenance and integrity checks.
These are security concerns for the AI system itself. They do not replace conventional controls such as network segmentation, secure authentication, patch management, backups, and incident response; they add additional things to test and monitor.
What oversight evidence says about risk assessment quality
The U.S. Government Accountability Office reviewed 16 critical-infrastructure sector AI risk assessments and one subsector assessment in its 2025 report. None of the reviewed assessments 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. GAO also found that a revised assessment template addressed some, but not all, of the gaps.
This is evidence of a weakness in the reviewed federal assessment process, not proof that every operator is unprepared. It does show why an inventory of use cases is insufficient: an operator needs a repeatable method that links each credible scenario to consequences, likelihood, controls, owners, and a decision about whether residual risk is acceptable. Read GAO-25-107435.
Why workforce and coordination are part of AI security
AI does not remove the need for OT expertise. Engineers understand process constraints, cybersecurity staff understand adversary behavior, data specialists understand model limitations, and vendors may understand proprietary equipment. A safe program assigns responsibilities across those groups instead of placing an AI tool in one team without operational accountability.
Recommended Free Tools
GAO’s 2024 review of CISA services and selected agencies found OT skill shortages and collaboration challenges involving service experience, information sharing, and interagency coordination. In the selected sample, 12 of 13 nonfederal entities reported positive experiences with CISA OT products and services, while seven identified negative experiences as a challenge. CISA officials also 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. These are dated findings from GAO’s reviewed entities, not current industry-wide staffing rates. GAO recommended measuring customer service, planning the OT workforce, and improving collaboration processes. Read GAO-24-106576.
How should critical-infrastructure operators assess AI risk?
Use a lifecycle assessment that treats safety, reliability, and availability as first-class outcomes alongside confidentiality and integrity. The following sequence is a practical starting point; sector regulation, safety engineering rules, and contractual requirements may add steps.
- Build an AI and dependency inventory. Record every model, vendor service, data source, hardware accelerator, software component, interface, update path, owner, and operating location. Include pilots and “shadow” analytics that are not yet in production.
- Map the decision and control boundary. Document what the system observes, recommends, changes, or commands; which OT assets it can reach; and what happens when it is wrong, delayed, manipulated, or unavailable.
- Define impact scenarios. Describe credible consequences for people, equipment, service availability, reliability, data confidentiality, and data integrity. Include cascading effects across connected facilities.
- Estimate likelihood and uncertainty together with impact. Use evidence about exposure, attacker access, model performance, data quality, and failure history. Do not assign a low risk merely because an event has not yet occurred.
- Test normal and abnormal conditions. Evaluate drift, missing or poisoned data, adversarial inputs, dependency outages, extreme operating states, and attempts to bypass safety processes. Test the complete workflow, not only the model in a laboratory.
- Map each material risk to a mitigation and owner. Controls may include segmentation, access restrictions, independent interlocks, input validation, signed model artifacts, monitoring, rate limits, human approval, backups, and a rollback procedure. Record who verifies that each control works.
- Set release and change gates. Require documented acceptance criteria before production, review retraining and software updates, and prohibit an unreviewed model change from silently altering a safety-critical decision.
- Exercise response and recovery. Rehearse model compromise, false outputs, loss of connectivity, and operator disagreement. Confirm that staff can switch to a known-safe manual or deterministic mode and restore trusted data and model versions.
- Reassess continuously. Review the system after model updates, new data sources, equipment changes, incidents, near misses, and changes in the threat environment. Process-model drift and rapidly changing AI attack techniques make a one-time approval inadequate.
Operational safeguards that make adoption safer
Keep safety authority independent
AI should not be the sole mechanism preventing a hazardous state. Preserve hard limits, interlocks, alarms, and shutdown functions that do not depend on the model. Where an AI output can influence an operator or control action, display its confidence, data freshness, and reason for escalation in a form the responsible person can evaluate.
Control data and model provenance
Maintain an auditable record of training and calibration data, preprocessing steps, model versions, evaluation results, and approvals. Validate sensor provenance and reject unexpected formats or ranges. Protect model files and prompts as operational assets, not as disposable application data.
Limit privileges and isolate failure
Separate development, testing, and production environments. Use read-only access where a recommendation does not require command authority, segment AI services from safety networks, and broker any permitted write action through an authenticated, logged control point. Design so a compromised analytics server cannot directly disable safety functions.
Best Value
Monitor for drift and misuse
Track input distributions, missing values, confidence changes, unusual query patterns, model latency, and divergence between model recommendations and operator actions. Establish thresholds that pause automated use and require review. Retain logs long enough to investigate an incident and demonstrate what the model knew at the time.
Make the fallback real
A “human in the loop” is meaningful only if the person has time, training, authority, and an independent way to verify the recommendation. Document who takes control during an outage, how to operate without the model, and how to restore a known-good version. Exercise those procedures under realistic workload and communications constraints.
Common adoption mistakes and corrective actions
| Mistake | Why it creates risk | Corrective action |
|---|---|---|
| Approving a model because its accuracy score is high. | Aggregate accuracy may hide rare but dangerous failures, drift, or poor performance in abnormal conditions. | Use scenario-based tests, safety limits, uncertainty thresholds, and operational acceptance criteria. |
| Treating a vendor-hosted AI service as ordinary software. | Remote dependencies, data handling, updates, and provider outages can affect OT availability and confidentiality. | Assess data flows and update authority, require service-level and incident terms, and maintain a local fallback. |
| Giving an AI agent broad write access for convenience. | A compromised prompt, model, account, or integration can turn a recommendation error into a physical disruption. | Start read-only, use least privilege and approval gates, and require reversible, logged changes. |
| Skipping an OT engineer in the security review. | A technically secure design may violate timing, maintenance, or safety constraints. | Use a cross-functional review led jointly by OT, cybersecurity, safety, data, and vendor specialists. |
| Performing one assessment at launch and never revisiting it. | Models, process conditions, dependencies, and attack methods change. | Trigger reassessment after updates, incidents, drift, architecture changes, and on a defined schedule. |
Policy and standards status to watch
Executive Order 14110 called for relevant agencies to assess critical-infrastructure AI risks at least annually, including potential critical failures, physical attacks, and cyberattacks, and to consider mitigations. Treat those statements as provisions of the order; verify current legal and policy status before using them as a present compliance requirement.
NIST is developing an AI Risk Management Framework profile for trustworthy AI in critical infrastructure. The NIST project description says the profile is intended to guide risk-management practices across AI and critical-infrastructure lifecycles and supply chains, but it is described as work in development rather than a finalized profile. Operators should use applicable current NIST material and sector requirements while monitoring that project for updates.
The 2025 OT guidance was produced by CISA and the Australian Signals Directorate’s Australian Cyber Security Centre with the NSA Artificial Intelligence Security Center, FBI, and partner agencies in Canada, Germany, the Netherlands, New Zealand, and the United Kingdom. Its emphasis on process safety, reliability, and operational controls complements NIST’s broader treatment of AI-system security.
Bottom line for infrastructure leaders
AI is neither automatically a security improvement nor automatically a new hazard. The decisive factors are where it is connected, what authority it receives, how failure affects physical services, and whether the organization can detect, contain, and recover from bad outputs. Separate operational AI from defensive AI, assess likelihood and impact together, preserve independent safety controls, test the full workflow, and fund the OT expertise and coordination needed to operate it. Reassessment must continue as models, data, software, equipment, and threats change.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
