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Protecting critical infrastructure from AI threats starts with knowing where AI is used, what it can affect, and how essential services will continue if it fails or is compromised. The U.S. Department of Homeland Security (DHS) recommends managing AI risk throughout a system’s lifecycle, with controls tailored to each sector and use case—not relying on one universal checklist or product.
What kinds of AI threats should infrastructure operators plan for?
DHS’s April 2024 Mitigating Artificial Intelligence (AI) Risk: Safety and Security Guidelines for Critical Infrastructure Owners and Operators groups the risks into three broad classes. They are categories for planning, not a list of confirmed attacks on infrastructure.
| Risk class | What it means for an operator |
|---|---|
| Attacks using AI | Malicious actors use AI to improve or scale attacks against people, systems, or services. |
| Attacks targeting AI systems | An attacker targets the AI system or its data, potentially affecting model behavior, availability, or the integrity of its inputs and outputs. |
| AI design or implementation failures | Problems in how an AI system is designed, configured, deployed, or used create safety, security, or operational risks even without a deliberate attack. |
The consequences depend on how a system is used. An AI tool that helps an analyst draft a report has a different risk profile from one whose output directly influences operational decisions. DHS’s guideline also reports that sector risk assessments identified more than 150 beneficial uses of AI across critical-infrastructure sectors in 2024. That figure describes identified use cases—not deployed systems, adoption rates, or attack frequency.
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The steps below organize practical actions around the Govern, Map, Measure, and Manage functions of the NIST AI Risk Management Framework, which DHS applies in its guidance. They are an operational sequence, not a verbatim DHS checklist. Treat the work as ongoing: risks can change as models, data, configurations, suppliers, and operational dependencies change.
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1. Assign owners and set rules for AI use
Name the people accountable for each AI use case, including both operational and security owners. Establish a policy for approving, deploying, changing, and retiring AI systems. Make sure AI-related incidents and system failures fit into existing incident-response, continuity, and information-sharing arrangements.
Clarify who may approve a system’s use, who can restrict or disable it, and how staff should report unexpected behavior. Responsibility should not be left implicit between the AI supplier, IT team, security team, and operational staff.
2. Inventory AI systems and their dependencies
Record current and proposed AI use—not only models built in-house. AI may be embedded in purchased software, equipment, or services. For each use, document:
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- The data sources and interfaces it uses, including connections to operational technology (OT) or other operational systems.
- Supporting services and dependencies, such as compute, networks, identity systems, and external data feeds.
- Where its output goes and whether a person reviews it before it can influence an operational action.
This inventory supports DHS’s Map function: understanding where, how, and why AI is being used.
3. Map the consequences of failure or misuse
For each use, trace what could happen if the system is unavailable, produces unsafe or misleading output, or is manipulated. Identify who relies on it, what decisions or functions it can affect, and whether essential services or safety could be disrupted. Include indirect effects—for example, an AI-supported process that affects a later operational decision even though it does not control equipment itself.
Assess each site and sector in context. DHS states that owners and operators should consider “sector-specific and context-specific AI risks and mitigations.” A generic risk label does not substitute for mapping the system’s actual role and dependencies.
4. Assess threats and failure modes
Use all three risk classes to prompt the assessment: attacks using AI, attacks targeting AI systems, and failures in design or implementation. Consider risks to data, model behavior, availability, and the operational process around the model. Include both deliberate actions and accidental or unintended failures.
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Do not treat a checklist as a precise, complete calculation of likelihood or risk level. The U.S. Government Accountability Office (GAO) reported that improvements to CISA’s assessment templates in August 2024 did not fully address identifying likelihood or evaluating the level of risk. Use the assessment to support informed decisions, document assumptions and uncertainty, and revisit them as conditions change.
5. Test and monitor before and during deployment
Set out how the organization will evaluate performance and risk before a system is deployed and while it is in use. Decide what evidence is needed to confirm that the system behaves acceptably for its intended task, how unexpected outputs or changes will be detected, and who reviews the results.
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Monitoring should account for changes to models, data, configuration, suppliers, and connected services—not just whether the system is online. DHS’s Measure function calls for assessing, analyzing, and tracking AI risks. Its guidance does not establish one universal test suite or threshold suitable for every sector, so operators need criteria appropriate to their own system and consequences.
6. Prioritize mitigations and account for suppliers
Address the risks with the most serious potential safety and service consequences first. Apply established IT and OT cybersecurity practices alongside AI-specific evaluation; AI controls do not replace basic security, operational safeguards, or applicable sector requirements.
CISA describes its Cross-Sector Cybersecurity Performance Goals as voluntary, prioritized baseline practices for critical-infrastructure IT and OT owners. They can complement an AI risk-management program. A CISA FAQ for the version discussed in the cited material said that version did not explicitly address AI; that statement is version-specific and should not be generalized to later versions. Assess suppliers and dependencies as part of the same risk picture, including what happens if a service or update is unavailable or untrusted.
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7. Prepare to fail safely and recover
Plan how essential work will continue if an AI component, its data, compute, or connected systems become unavailable or untrusted. DHS identifies operational resilience measures including backup systems, manual or non-AI alternatives, continuity plans, and crisis exercises.
Decide who can override or disable an AI component, what fallback process is available, and how staff will know when to use it. Test backups and recovery procedures. A manual alternative is useful only if it is feasible and safe for the specific operation; design and practice it for the site rather than assuming every system can be run manually.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should operators decide which risks to address first?
Prioritize based on the system’s real operational role and the consequences of failure, not on whether it is labeled “AI.” For each use, consider:
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- Service and safety consequences: What essential function or safety outcome could be affected?
- Operational influence: How directly can the AI output affect operations, and is there a meaningful human review or override?
- Dependencies: Which data, models, suppliers, networks, and supporting services must be trustworthy and available?
- Validation and detection: Can the organization check performance and detect manipulation, degradation, or drift?
- Recovery: How quickly must the function be restored, and has a non-AI or manual alternative been tested?
- Applicable requirements: How does the use fit sector-specific regulation and established IT and OT controls?
Record the reasoning, owners, and actions for each significant risk. Reassess when the use case, system, supplier, or operating environment changes. The DHS framework treats risk management as continuous work across the AI lifecycle, rather than a one-time approval.
What should operators take from federal guidance?
DHS’s April 2024 guidance provides a cross-sector structure for governing, mapping, measuring, and managing AI risk; it does not prescribe a universal set of technical controls for every infrastructure operator. CISA’s Cross-Sector Cybersecurity Performance Goals offer a voluntary IT and OT baseline, while GAO’s review cautions that assessment methods may not fully resolve likelihood and risk-level judgments. Use these materials together with the requirements and operating realities of the relevant sector and site.
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