Build AI-assisted threat detection as a safety-conscious operational workflow, not as an autonomous controller. Start with the facility’s assets, physical processes and response authority; monitor OT-compatible signals against expected behavior; use AI only to help analysts organize and interpret observations; and connect reviewed findings to exercised response and recovery plans. An AI-generated alert is a reason to investigate, not proof of malicious activity or permission to change a physical process.
The guidance is evolving. As of October 9, 2026, NIST SP 800-82 Rev. 4 is an initial public draft published September 21, 2026, with comments due November 30, 2026; Rev. 3, published in September 2023, remains the final edition. Check the Rev. 4 draft record for status and use the final Rev. 3 guidance as the published edition. The distinction matters because OT security must account for the performance, reliability and safety requirements of systems that monitor or directly change the physical environment.
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What the workflow should—and should not—do
Operational technology (OT) includes systems that monitor or cause changes in the physical environment. Industrial control systems are one example; other systems that monitor or control physical processes also fall within this security context. A detection workflow therefore has to consider consequences for service continuity and safety, not only whether a network event looks suspicious. NIST’s final SP 800-82 Rev. 3 describes its purpose as securing OT while addressing unique performance, reliability and safety requirements.
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- Appropriate authority for AI: organize, enrich, summarize or prioritize information for human review, within a documented scope.
- Authority that requires a separate approved process: changing controller state, isolating an operational asset or otherwise affecting a physical process. Do not grant such authority to an alert or model output alone.
- Required fallback: maintain conventional monitoring and incident-response paths if the AI service, model or data feed is unavailable or cannot be trusted.
This conservative boundary follows from OT’s safety and reliability concerns and from guidance to plan for incident response and continuity. NIST also describes AI as dual-use: it can aid defenders while potentially strengthening attackers. The AI components, their interfaces, data and outputs therefore belong in the security assessment, not outside it. See NIST’s overview of AI security and resilience.
Set the mission, limits and ownership before selecting AI
Define what protection means at this site
Write down the service and physical processes the workflow is intended to protect, the critical assets involved, and the IT/OT dependencies that could affect them. Identify operational owners, security contacts, safety constraints, escalation authority and who is on call. This gives analysts a basis for judging the operational importance of an observation instead of treating every anomaly score as equally urgent.
Make the workflow’s permitted and prohibited actions concrete. For example, it may attach asset and maintenance context to a network alert and put the result in an analyst queue. It should not directly change a controller’s state or isolate an operational asset unless a separately approved procedure explicitly authorizes the action and addresses its operational effects. An alert does not grant that authority.
Document AI use and risk management
NIST’s AI Risk Management Framework (AI RMF) is voluntary, cross-sector guidance for managing AI risks across design, development, use and evaluation. It is not a certification that a system is safe or secure. NIST says the framework is being revised; its April 7, 2026 announcement of a concept note for a Trustworthy AI in Critical Infrastructure profile describes profile-development work, not a finalized sector-specific standard. See the AI RMF page and the AI RMF 1.0 publication record.
Use that risk-management perspective to document the intended use, known limitations, data sources and lineage, output reviewers, evaluation approach, and what staff do when inputs are missing or the AI is unavailable or untrusted. Include access to the model and its supporting data in the security design. NIST’s framework is intended to help organizations manage AI risk and promote trustworthy, responsible development and use; adopting it by itself does not prove the facility’s implementation is trustworthy.
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Build asset and process context analysts can use
Maintain a critical-asset inventory
Keep a current inventory of critical assets and systems, including their owners, purpose, location or zone, communication relationships and operational importance. CISA identifies an updated critical-asset inventory as a capability to consider when evaluating ICS/OT monitoring technology. The inventory should give a reviewer enough context to answer: what is affected, what process depends on it, and who can verify whether the activity is expected?
Record what normal operations look like
Enrich telemetry with authorized maintenance windows, approved engineering activity, known process states and expected communications. A legitimate maintenance action may look unusual without its authorization context; a network deviation alone cannot establish intent. Keep the context current enough for analysts to check it during triage, and define who is responsible for updating it when assets, processes or approved activity change.
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Prioritize by potential operational impact, not by a generic model score alone. CISA’s voluntary Cross-Sector Cybersecurity Performance Goals can help structure baseline practices around functions such as Identify, Detect, Respond and Recover. They are a planning aid, not a substitute for site-specific safety and operational decisions.
Collect OT-appropriate signals and establish expected-behavior baselines
Evaluate monitoring for the facility’s environment
Choose monitoring technologies that are designed for ICS/OT assets and can analyze the facility’s relevant ICS network traffic, data and communications conforming to ICS protocols. CISA’s ICS/OT monitoring technology considerations identify capabilities to evaluate, including discovery and maintenance of a current inventory, establishing expected network-traffic baselines, comparing observations against those baselines, and alerting on deviations.
Check fit against actual protocols, devices and network architecture rather than assuming that a generic monitoring approach will see the signals that matter. Baseline dimensions can include ports, protocols, services, communicating devices, traffic volumes and timing. The monitoring design should be compatible with site architecture and operational constraints; do not introduce a collection method or change without assessing its effect on the environment.
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Use passive collection only where it fits
A passive network TAP can be one way to provide traffic to a sensor in some architectures. That is an implementation possibility, not a universal CISA recommendation or a requirement for every facility. Confirm topology, device characteristics and engineering approval before choosing a collection method. The goal is useful visibility without creating an unacceptable operational impact.
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Use AI as an analyst aid, with evidence visible
Limit the AI task to a testable purpose
Begin with one or more bounded assistance tasks: group alerts that appear related, attach relevant asset and maintenance context, summarize observations for a reviewer, or prioritize baseline deviations for investigation. Treat each as a design hypothesis to evaluate on the facility’s own data and operating conditions—not as a sourced claim that the AI will reliably detect attacks.
Do not rely on a generated narrative or opaque score as the sole basis for classifying an event. The analyst should be able to inspect the underlying telemetry and understand what observations caused an alert to be raised. Present uncertainty and missing context plainly enough that a reviewer can distinguish evidence from interpretation.
Protect inputs, outputs and availability
Assess the confidentiality, integrity, availability and resilience of the AI components and the data and interfaces they depend on. Restrict and monitor access in line with the facility’s security design, and consider how corrupted, incomplete or unavailable data could affect the output. Decide in advance how work proceeds when an AI service fails or becomes untrusted; analysts should still have a conventional route to monitoring evidence and response procedures.
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NIST’s AI RMF provides a voluntary structure for considering risk through AI design and use, while its security-and-resilience work highlights that AI changes the cybersecurity risk picture for both IT and OT. Neither source establishes a universal AI architecture or outcome threshold for a particular facility. Local technical and operational evaluation remains necessary.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Route alerts through a defined triage and investigation process
Capture enough information to validate the alert
For each alert, record the signal, affected asset and process, supporting evidence, uncertainty or confidence information, and analyst disposition. Make the relevant underlying telemetry available rather than requiring a reviewer to rely on a generated summary. Check authorized maintenance and administrative activity before declaring an incident.
CISA’s federal incident playbook search result emphasizes defined processes, baseline information and procedures to deconflict a suspected event from authorized activity. That playbook is tailored to federal civilian agencies, so private operators can use those ideas as an operational reference, not as a binding rule for every organization. The site’s own authorities and incident procedures determine who validates and escalates an alert.
Keep a record that supports learning
Record false alarms, data gaps, review decisions and events discovered later that were not surfaced as expected. Use those records to investigate whether the issue came from missing asset context, a stale baseline, a telemetry gap, an unsuitable AI task or a review-process problem. Do not silently convert uncertain output into a confirmed incident label.
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Connect detection to response, continuity and recovery
Detection is useful only when the organization can respond without creating additional operational danger. Connect the workflow to a cyber incident response plan, resilience plan and continuity-of-operations plan. CISA, the FBI and the NSA advise critical infrastructure organizations to create, maintain and exercise these plans. Their January 11, 2022 guidance specifically calls for regularly testing contingency plans, including manual controls, so safety-critical functions can be maintained during a cyber incident.
Define the approved review and escalation steps an AI-assisted alert initiates; it should not trigger an unreviewed command to change a physical process. Response planning should account for IT/OT dependencies, safe isolation procedures where needed, and how operators will function if access to or control of the environment is lost. Exercise the relevant contingency procedures with the people who would use them, rather than treating a written plan as proof that the fallback is workable.
- Identify who is authorized to validate a suspected OT incident and who can approve any operationally consequential response.
- Specify how the team will keep safety-critical functions operating if normal access or control is lost.
- Exercise manual controls and other relevant contingency plans, then use lessons from the exercise to improve the response path.
Evaluate locally before relying on the workflow
Test with representative facility data and scenarios
Before making an AI-assisted process operationally relied upon, evaluate it using representative facility data and scenarios with OT and security personnel. Check whether the workflow reaches the right team, gives analysts evidence they can validate, distinguishes expected authorized activity from items needing investigation, and leaves response procedures safe and usable. These are locally meaningful evaluation dimensions, not published NIST or CISA performance thresholds.
Do not mistake model metrics for operational readiness
Assess the entire path from telemetry collection through review, escalation and fallback. A model output that looks useful in isolation does not show that the asset context is current, the alert reaches the right person, or the response can be performed safely. Evaluate the effects of missing or delayed data and AI-service unavailability as part of the same workflow.
The cited sources provide no verified statistic for how much AI improves threat detection in critical infrastructure, and they establish no universal acceptable false-positive, missed-detection or response-time rate. Do not use a generic accuracy figure as a substitute for facility-specific evidence. Set evaluation criteria that fit the organization’s processes and safety obligations, document results and limitations, and reassess when operating conditions or the workflow change.
Quick Recap
Practical build sequence
- Define the protected service and authority. Map the processes, critical assets, IT/OT dependencies, safety constraints, owners and escalation roles. Document what AI may assist with and what it may not do without a separate approved procedure.
- Assemble the context. Maintain the critical-asset inventory and connect it to known process states, authorized maintenance, approved engineering activity and expected communications.
- Choose and validate monitoring. Confirm that the approach can analyze relevant ICS traffic and protocols, supports inventory and expected-traffic baselines, and fits the site’s topology and operational constraints.
- Establish the baseline and alert path. Define expected behavior and which deviations should reach an analyst, ensuring authorized changes can be checked and deconflicted.
- Add bounded AI assistance. Select a specific analyst task, expose its supporting evidence, record known limitations, and retain a usable conventional path if the AI or its data is unavailable or untrusted.
- Exercise triage and response. Test alert review, escalation, continuity and relevant contingency procedures with the personnel who own those actions, including manual controls where applicable.
- Evaluate and improve. Review alert handling, data gaps, false alarms, missed events and safe usability; document findings and adjust context, monitoring, AI scope or response procedures as needed.
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