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What AI threats should infrastructure operators plan for?
The U.S. Department of Homeland Security (DHS) groups AI risk to critical infrastructure into three broad classes. They are useful categories for planning, not a list of confirmed incidents:
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- Attacks using AI: malicious actors may use AI to enhance or scale attacks against infrastructure.
- Attacks targeting AI systems: an attacker may target an AI system or the data it relies on.
- Design or implementation failures: an AI system may create safety or security problems because of weaknesses in how it is designed, configured, deployed, or used.
For an operator, the important question is what the system can affect. A tool that summarizes information for an analyst has different potential consequences from one whose output can influence operational control. A failure might affect the model itself, its data, its availability, or the operational process that depends on its output.
DHS reported more than 150 beneficial AI use cases identified across critical-infrastructure sectors in its 2024 guideline. That figure describes identified uses, not the number of deployed systems, adoption rates, or incidents. It does underscore why operators should look beyond locally built models and consider AI embedded in products and services they purchase.
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Seven steps to manage AI risk in critical infrastructure
The following seven actions translate the Govern, Map, Measure, and Manage functions of the NIST AI Risk Management Framework, as applied in DHS’s April 2024 guidance, into an operational sequence. They are not a verbatim DHS checklist. Risk management should continue throughout an AI system’s lifecycle, not stop at procurement or launch.
1. Assign ownership and set rules
Name the people responsible for operational decisions about AI and for security oversight. Set an organizational policy that explains who may approve a use, how risks are reviewed, and how incidents or system failures enter existing response and information-sharing arrangements.
- Identify who can approve, restrict, suspend, or retire an AI use.
- Clarify the responsibilities of operations, security, safety, procurement, and relevant business teams.
- Include AI-related failures and incidents in established escalation and response processes.
Clear ownership matters when a system produces an unsafe result or becomes unavailable: staff need to know who has authority to act, rather than assuming responsibility sits solely with a vendor or technical team.
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Record current and proposed AI uses, including AI built into purchased products and services. For each use, capture the system owner, vendor, model, data sources, interfaces, supporting services, and operational dependencies. Note whether the system is advisory or can directly affect a decision or process.
Keep the inventory useful for response and change management. A system’s risk can depend on more than its model: a connected service, data feed, supporting compute, or change to configuration may affect whether the system can be trusted or remain available.
3. Map consequences in your own environment
For each use, document what the system can affect, who relies on it, and what could happen if its output is unsafe, misleading, unavailable, or delayed. Identify any safety functions or essential services that could be disrupted, and consider how the system fits into the surrounding operational process.
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Apply this analysis to the specific sector, site, and use case. DHS emphasizes that AI risks and mitigations are context-specific and sector-specific; a generic risk label does not establish how serious a failure would be at a particular facility.
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Use all three threat classes—attacks using AI, attacks targeting AI systems, and failures in design or implementation—to examine the model, its data, its availability, and the process around it. Consider what an attacker or an ordinary failure could change, disrupt, or cause staff to rely on incorrectly.
Do not treat a checklist as a settled quantitative risk answer. The U.S. Government Accountability Office (GAO) reported that improvements to CISA’s assessment templates in August 2024 did not fully address how to identify likelihood or evaluate overall risk level. Operators should document uncertainty and the basis for their judgments rather than imply greater precision than their method supports.
5. Test and monitor before and during use
Define how the organization will test, measure, and track performance and risk before deployment and while the system is in service. Set review triggers for changes to models, data, configuration, interfaces, or dependencies, and decide who reviews results and what action follows when performance or trustworthiness is in question.
DHS’s Measure function calls for assessing, analyzing, and tracking AI risks. Its guidance does not establish one universal test suite or threshold for every infrastructure sector. The appropriate checks depend on the system’s purpose and possible consequences.
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6. Prioritize safeguards and review suppliers
Address risks according to the seriousness of their potential safety and service consequences. Apply established information-technology (IT) and operational-technology (OT) cybersecurity practices alongside AI-specific evaluation. Review what suppliers provide about the system, its dependencies, changes, and support, and account for that information in the organization’s own risk decisions.
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CISA describes its Cross-Sector Cybersecurity Performance Goals as voluntary, prioritized baseline practices for infrastructure IT and OT owners. They can complement AI risk management, but they are not a substitute for assessing an AI use in context. A CISA FAQ’s statement that the then-current CPG version did not explicitly address AI is version-specific; it should not be assumed to describe a later version.
7. Prepare to fail safely and recover
Plan for the AI component, its data, compute, or connected services to become unavailable or untrusted. Determine who may disable or override it, how affected staff will be notified, and how essential operations can continue. Where feasible, maintain tested backups, continuity arrangements, crisis exercises, and manual or non-AI alternatives.
DHS identifies backup systems—including manual and non-AI options—continuity plans, and crisis exercises as resilience measures. A manual fallback is not automatically safe or feasible at every site: it must be designed for that environment and practiced so people understand its limits.
How to decide which AI uses need the strongest controls
Prioritize safeguards based on the role and consequences of a system, rather than treating every AI use as equally risky. For each proposed or existing use, examine:
- Service and safety consequences: What essential function could be affected if the system fails or produces an unsafe output?
- Operational influence: Is the system providing information to a person, or can its output directly affect an operational process? Who can override it?
- Dependencies: What data, models, vendors, connectivity, compute, and supporting services must work for the system to remain available and trustworthy?
- Ability to validate: Can the operator test performance and detect relevant changes, drift, or manipulation?
- Recovery options: How quickly must service resume, and are non-AI or manual alternatives available and tested?
- Applicable obligations: How does the use fit sector-specific requirements and existing IT and OT controls?
These considerations help direct effort toward the uses where failure could matter most. They do not replace a site- and sector-specific risk assessment.
Use federal guidance as a framework, not a guarantee
DHS’s April 2024 Mitigating Artificial Intelligence (AI) Risk: Safety and Security Guidelines for Critical Infrastructure Owners and Operators applies the NIST AI Risk Management Framework functions: Govern, Map, Measure, and Manage. DHS describes risk management as continuous across the AI lifecycle and says operators should consider sector-specific and context-specific AI risks and mitigations.
GAO’s August 2024 reporting adds an important qualification: improvements to CISA’s assessment templates did not fully resolve likelihood identification and risk-level evaluation. The available guidance therefore supports a structured process, but not a universal numerical method that can settle every operator’s risk. CISA’s 2023–2024 roadmap is historical strategic context; claims about the current status of agency programs or later revisions should be checked against the relevant agency publication.
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