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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteAI is not formally designated critical infrastructure under one universal legal regime. But it is becoming strategically critical because its operation depends on interconnected systems—electricity, data centers, networks, storage and semiconductors—and because operators are beginning to use AI to monitor and defend essential services. Securing AI therefore means protecting both the infrastructure AI relies on and the AI systems being brought into critical operations.
Is AI critical infrastructure?
It is more accurate to call AI a critical-infrastructure dependency and capability than to say that AI itself has been universally designated critical infrastructure. The distinction matters: legal definitions and duties depend on the country, sector and specific rule. The strategic case, however, is clear. AI services rely on physical and digital infrastructure, while energy and other critical-sector operators are exploring AI to help detect threats and maintain operations.
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This relationship runs in both directions. A disruption to power, connectivity, data storage or computing capacity can interrupt AI services. A compromised AI system used in an operational environment can, in turn, create risks for the service that depends on it. AI security and infrastructure security increasingly overlap, but neither replaces the other.
What infrastructure does AI depend on?
AI training and inference require a connected stack, not just a model or a data center. The July 23, 2025 White House order illustrates the breadth of that stack for a particular U.S. federal permitting initiative: it identifies energy infrastructure, backup power, semiconductors, networking equipment such as switches and routers, and data storage as components of covered data-center projects.
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| Layer | What it contributes | Security and continuity concern |
|---|---|---|
| Compute facilities | Space and systems to run AI training, inference and related workloads. | Physical access, facility operations and dependence on supporting services. |
| Energy and transmission | Power for computing equipment, cooling and associated infrastructure. | Loss or disruption of power can interrupt the workload even if its software remains intact. |
| Semiconductors | Hardware used in computing systems. | Hardware availability and integrity are part of the infrastructure dependency, not a separate concern from AI security. |
| Networking | Connectivity among systems and to users or other services; the White House order names switches and routers. | Network disruption or compromise can affect access, communications and operations. |
| Storage and data | Data used by AI systems and storage for software, models and operational information. | Confidentiality, integrity and availability matter for data, models and outputs. |
The order defines a “Data Center Project” for its own purposes as a facility requiring greater than 100 megawatts (MW) of new load dedicated to AI inference, training, simulation or synthetic data generation. That is a threshold in this specific U.S. federal order—not a global definition, a general measure of AI data-center size, or an observed statistic about infrastructure demand.
How does AI affect critical-infrastructure cybersecurity?
AI changes the security picture in three directions: it can be a target, a tool for defenders, and a way for attackers to expand their capabilities. NIST notes that AI systems face familiar information-security risks to confidentiality, integrity and availability across systems, training data and output data. It also identifies AI-specific concerns, including evasion, model extraction and membership inference. These risks may arise alongside ordinary software, network, data and physical-security threats.
| Security question | What it means in practice |
|---|---|
| Secure from AI | Defend people, systems and infrastructure against AI-enabled attacks. |
| Secure with AI | Use AI to support defensive work such as threat detection, threat hunting, operational-technology and industrial-control-system visibility, anomaly detection and incident response. |
| Secure AI | Protect AI systems used to operate, control or defend essential services. |
These three phrases are the pillars of the U.S. Department of Energy’s CESER AI-FORTS program. The program describes work with national laboratories, utilities, OT/ICS operators and research institutions. Using AI for defense does not remove the need to secure the AI tool itself; nor does deploying AI automatically make an operator more secure.
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How do you secure AI infrastructure?
Security needs to follow the dependencies and lifecycle of the system. The following operator steps are a practical way to apply NIST’s security and resilience guidance and the operational concerns reflected in DOE’s AI-FORTS program; they are not a substitute for sector-specific legal requirements.
- Map the service and its dependencies. Identify which AI workloads support which services, where they run, and their dependencies on facilities, energy, networking, semiconductors, storage, data, software and people. Include external providers and operational technology where relevant. Mark which dependencies are essential to safe, continuous operation.
- Assign owners and define acceptable operation. Establish accountable owners for the AI system, its data and models, the underlying infrastructure, and any operational decision it supports. Define what the system may do, when a human must review an output, and what operators should do if a component becomes unavailable or untrusted.
- Protect confidentiality, integrity and availability across the lifecycle. Apply controls to system access, training and operational data, model components, configuration settings, software and outputs. Limit access to what each user or service needs, protect sensitive information, and manage changes so that unauthorized or unsafe alterations can be detected and addressed.
- Assess AI-specific attack paths. Consider how inputs might evade detection or manipulate a model, how a model could be extracted, and whether outputs could reveal information about training data. Assess these risks in the actual deployment context rather than treating a general-purpose AI model as interchangeable with a system connected to critical operations.
- Secure the physical and operational environment. Include facilities, power and backup arrangements, networks, storage, hardware, OT/ICS connections and staff procedures in the security boundary. A protected model cannot keep a service available if its facility or supporting infrastructure fails.
- Prepare to detect, respond and recover. Establish monitoring, escalation, incident-response roles and recovery priorities for both AI-specific and conventional incidents. Plan for degraded operation and for maintaining or restoring critical functions when a system is compromised, not only for preventing an intrusion.
- Review controls as the system changes. Reassess risk when models, data, software, hardware, providers, connections or operational uses change. Track incidents and near misses so that controls and recovery plans reflect real operating conditions.
NIST’s AI Risk Management Framework (AI RMF) is voluntary guidance intended to help organizations incorporate trustworthiness into AI design, development, use and evaluation. NIST identifies “Secure and Resilient” as a primary characteristic of trustworthy AI. Its security work also recognizes that AI’s attack surface is complex and not yet comprehensively addressed by existing frameworks. These considerations support lifecycle-wide risk management, but the framework itself should not be mistaken for a universal binding rule.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do current U.S. policy and guidance establish?
The policy documents cited here are U.S. examples. They do not create a single global AI-infrastructure law, and obligations for a particular operator must be checked against the applicable jurisdiction and sector.
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- NIST AI RMF: NIST released AI RMF 1.0 on January 26, 2023, as voluntary guidance. NIST says the framework is being revised. On April 7, 2026, it released a concept note for a profile on trustworthy AI in critical infrastructure. That profile was in development at the time described; it was not a finished mandatory standard.
- AI vulnerability management: A June 6, 2025 White House order directed agencies to incorporate management of AI software vulnerabilities and compromises into existing vulnerability-management and interagency coordination processes. It called for incident tracking, response, reporting and sharing indicators of compromise for AI systems, and set a November 1, 2025 deadline. The directive is evidence of what agencies were instructed to do, not proof that every agency completed the work.
- Data-center project definition: The July 23, 2025 White House order set the greater-than-100-MW new-load threshold described above for a “Data Center Project” under that order. It also revoked Executive Order 14141, dated January 14, 2025. The earlier order should not be treated as the current policy for this initiative.
For operators, the practical lesson is to separate voluntary frameworks, agency directives and binding obligations. A useful security framework can inform internal controls without itself imposing a legal duty; an executive directive to federal agencies does not automatically establish the same requirements for every private operator or for other countries.
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Start with the services whose interruption would matter most, then trace their dependencies and decide how they can remain safe when technology or infrastructure fails. A mature approach should connect AI-system protections with facility, energy, network, data and operational controls; cover confidentiality, integrity and availability; and include detection, response and recovery alongside prevention. Where AI supports critical operations, human oversight and a workable fallback are part of resilience, not afterthoughts.
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