AI red teaming can expose unsafe model behavior, misuse paths and weaknesses in AI-enabled decision-making, but the available evidence does not show that an AI red-team exercise has already found a specific vulnerability in a live industrial control system (ICS). The clearest lesson is methodological: evaluate the AI system and the surrounding operational technology (OT) environment separately, then test how failures could affect detection, containment, reliability and safety. Conventional cyber red-team assessments add evidence about organizational defenses, but they are not substitutes for AI testing or proof of a sector-wide ICS weakness.
What the evidence actually shows
Red teaming is an evaluation method, not a universal property of a technology. A result is bounded by the system, environment, permissions, scenarios and actions that were tested. A model tested in a laboratory is not the same object as a production control network, and an assessment of an enterprise network cannot establish how every power, water, manufacturing or transportation operator would perform.
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The distinction matters because two different activities are often called red teaming. AI red teaming probes an AI model or AI-enabled application for vulnerabilities, misuse potential and undesirable behavior. A conventional cyber red team emulates an adversary against an organization’s people, processes, networks and security controls. CISA’s AI roadmap calls for better security practices and red-teaming approaches for AI, while its published critical-infrastructure advisories describe cyber teams testing organizational defenses. Those sources should be read together, not treated as evidence of one test type.
CISA’s 2023–2024 Roadmap for Artificial Intelligence identifies a concern that AI deployment in critical-infrastructure sectors could increase exposure to critical failures, physical attacks or cyberattacks. That is a risk focus and planning direction, not a finding that a named AI model has already compromised an industrial controller.
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AI red teaming and cyber red teaming are different tests
| Axis | AI-system red teaming | Conventional cyber red teaming |
|---|---|---|
| Object under test | An AI model or AI-enabled system, including its data, tools, interfaces and safeguards | An organization’s people, processes, networks and security controls |
| Primary aim | Find AI flaws, misuse paths, unsafe outputs or undesirable behavior | Emulate adversary activity and assess prevention, detection, containment and response |
| Relevant evidence | CISA’s AI roadmap and NIST’s ARIA evaluation program | CISA critical-infrastructure assessment advisories |
| ICS conclusion supported by the cited material | No direct live-ICS test result is established | Findings illustrate organizational defense gaps, not universal ICS weaknesses |
NIST’s ARIA program separates model testing, red teaming and field testing. That separation is useful for an OT program: a model can pass controlled prompts yet fail when connected to real workflows, unfamiliar data or human operators. Field testing can reveal operational effects, but it still has to be scoped so that an experiment cannot send an unsafe command to equipment.
A combined program therefore asks two questions. First, can the AI be manipulated, confused or induced to produce an unsafe recommendation? Second, can the organization detect and contain that behavior before it affects a process? Answering only one leaves a blind spot.
How AI can increase critical-infrastructure exposure
AI is increasingly proposed for asset inventory, alarm triage, maintenance advice, engineering documentation, incident analysis and operator assistance. Each use can introduce a different failure mode. CISA’s roadmap highlights the possibility that AI adoption could increase exposure to critical failures, physical attacks or cyberattacks in critical-infrastructure sectors.
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- Prompt or data manipulation: An attacker who alters documents, telemetry, tickets or prompts may steer the model toward a harmful conclusion without directly exploiting a controller.
- Excessive authority: An assistant connected to historians, engineering tools or remote-access systems becomes more consequential if it can execute actions rather than merely provide advice.
- Confidentiality and integrity loss: Sensitive process diagrams, credentials, recipes or incident records can be exposed through prompts, logs, retrieval systems or model outputs.
- Availability and dependency: Operators may lose a decision aid during an outage, or a poorly behaving automation layer may consume attention and delay manual response.
- Automation bias: A confident answer can receive more trust than an uncertain one, especially during an alarm flood or time-critical event.
These are threat hypotheses to test, not documented ICS exploits from the cited material. A useful red-team plan maps each hypothesis to a consequence: loss of view, loss of control, unsafe state, prolonged outage, compromised data integrity or delayed incident response.
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What CISA’s conventional assessments reveal
The 2024 assessment
In Enhancing Cyber Resilience: Insights from CISA Red Team Assessment, CISA describes an assessment requested by a U.S. critical-infrastructure organization. The team obtained initial access through a web shell left by a third party’s earlier security assessment, moved from the demilitarized zone into the organization’s network, and compromised the domain and sensitive business systems.
The organization had evidence of initial activity but did not act promptly on malicious traffic or sufficiently challenge the team’s presence. CISA’s lessons include insufficient technical controls and excessive reliance on host-based endpoint detection and response without enough network-layer protection. The assessment demonstrates why a mature-looking security program should be tested against realistic adversary behavior: controls can generate signals without producing timely investigation, escalation and containment.
Those findings belong to the assessed organization. The advisory does not describe an AI red-team exercise, a test of industrial controllers or a universal condition across critical infrastructure.
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The 2023 assessment
A separate CISA assessment advisory describes persistent access and lateral movement at a large critical-infrastructure organization with geographically separated sites. CISA recommends monitoring, regular assessment and testing to ensure that security processes can detect and mitigate adversary activity. Again, this is evidence about conventional cyber defense and response, not an AI evaluation or a direct test of a control loop.
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Together, the advisories support a practical conclusion: detection coverage must be demonstrated under adversary pressure, and network visibility should complement endpoint telemetry. They do not justify claiming that every ICS operator has the same weakness.
Why OT and ICS constraints change the test design
NIST defines operational technology as programmable systems or devices that interact with the physical environment by monitoring or changing devices, processes or events. ICS is one OT category; others include building automation, transportation and physical-access systems.
The NIST SP 800-82 Rev. 4 initial public draft, published September 21, 2026, addresses OT security while accounting for unique performance, reliability and safety requirements. Its listed comment deadline is November 30, 2026, so it should be treated as a draft during that period rather than as final guidance.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsIn practice, an IT-style exercise that assumes systems can be scanned, restarted or patched on demand may be unacceptable in a plant. A test can affect deterministic timing, vendor support, safety instrumented functions, environmental limits or production quality. The safest objective is not “break the system”; it is to obtain evidence about exposure and response while preserving a defined safe state.
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How to red-team an ICS safely
Do not probe live industrial equipment without written authorization, a safety review and an agreed stop procedure. The following sequence keeps the exercise useful without turning it into unsupervised experimentation.
- Set authority and boundaries. Name the asset owner, safety authority, test leader and emergency contacts. Define permitted facilities, networks, accounts, time windows and data handling. Explicitly exclude safety systems, protective relays or production actions that cannot be safely exercised.
- Define success in operational terms. Choose questions such as whether a malicious change would be visible, how quickly an operator would be alerted, whether remote access can be revoked, and whether the process can continue in a known safe mode. Do not use “obtain maximum access” as the only objective.
- Build an asset and dependency map. Document controllers, supervisory systems, engineering workstations, historians, identity services, remote-access paths, safety systems and the IT/OT boundary. Record which assets are safety-critical, latency-sensitive or unsupported by normal scanning tools.
- Create a threat model for AI and conventional attackers. Include compromised vendor access, stolen credentials, malicious documents, manipulated telemetry, prompt injection, data poisoning and an operator receiving an unsafe recommendation. Assign each scenario a plausible consequence and an escalation limit.
- Start in a representative lab or digital twin. Reproduce network zones, authentication, model integrations and alerting with synthetic or sanitized data. Validate that test traffic, prompts and outputs cannot reach production actuators.
- Exercise the AI layer without granting control authority. Test whether the model reveals secrets, follows hostile instructions embedded in documents, invents values, ignores uncertainty, recommends unsafe changes or bypasses approval rules. Capture inputs, retrieved material, model version, tools called and final output for review.
- Test detection and response with controlled signals. Use pre-approved benign indicators, simulated account misuse and tabletop actions to check network monitoring, endpoint telemetry, identity alerts, operator notification and escalation. Keep the exercise reversible and avoid changing live process values.
- Apply stop criteria immediately. Stop if an unexpected process effect, safety alarm, loss of control, equipment instability, vendor-support risk or unplanned data exposure appears. The authority to stop must be independent of the team’s desire to complete a scenario.
- Report evidence and remediation. Record the tested scope, assumptions, timestamps, observed controls, missed signals, business and safety impact, and residual uncertainty. Separate confirmed observations from hypothetical consequences. Assign owners and retest after corrective changes.
What an AI-focused ICS test should examine
Input and context integrity
Check whether the model can distinguish trusted telemetry and engineering records from user-supplied text or stale documents. Test conflicting values, missing timestamps, unit changes and manipulated maintenance notes. A safe system should expose uncertainty and provenance instead of silently blending sources.
Instruction hierarchy and prompt injection
Feed the system documents containing instructions that conflict with the approved task. The expected behavior is to treat untrusted content as data, preserve system rules and request human review for consequential actions. The test should measure whether an injection changes a recommendation, tool call or approval path.
Tool and privilege boundaries
Inventory every connector available to the model, including historians, ticketing systems, engineering databases and remote-access platforms. Verify least privilege, separate credentials, approval gates and complete logging. An assistant that can draft a change is materially different from one that can deploy it.
Safety and uncertainty behavior
Use scenarios in which the correct answer is “do not proceed.” Test ambiguous alarms, sensor disagreement, out-of-range values and requests to disable an interlock. Evaluate whether the model recommends a safe fallback, identifies the responsible procedure and escalates to a qualified operator.
Resilience and recovery
Assess behavior when the model, retrieval service, network connection or identity provider is unavailable. Operators should retain a documented manual procedure and should not be forced to improvise because an AI feature has become unavailable.
Controls that reduce the risk
NIST’s SP 1800-10 addresses information and system integrity in manufacturing ICS environments. It notes that connecting OT and IT can expand business capabilities and remote access while also creating opportunities to compromise ICS and data integrity. Its example solutions are a useful design reference, not a claim that one architecture fits every facility.
CISA’s ICS Recommended Practices collection provides implementation guidance covering defense in depth, incident response, patch management, control-system remote access and related topics. Apply those practices according to the facility’s operating and safety requirements.
- Segment by consequence: Separate corporate IT, industrial DMZ, supervisory networks, control networks and safety-related systems where the process requires it. Restrict pathways, deny unnecessary east-west movement and monitor approved conduits.
- Use layered visibility: Combine endpoint data with network-layer monitoring, identity events, remote-access logs and process-aware alarms. The 2024 CISA assessment shows why endpoint detection alone may not reveal the full path of an intrusion.
- Harden remote access: Require named accounts, strong authentication, time-bounded approvals, session recording and rapid revocation. Treat vendor access as a monitored pathway, not a permanent exception.
- Manage patches by risk: Maintain an inventory, test updates in a representative environment, coordinate with vendors and use compensating controls when immediate patching would threaten availability or safety.
- Protect integrity: Sign or otherwise validate configuration and engineering changes, restrict write access, preserve trusted backups and alert on unexpected logic, firmware or set-point changes.
- Practice incident response: Define who can isolate a zone, move to manual operation, contact the process owner and preserve evidence. Exercise those decisions before an AI-related event occurs.
- Govern AI explicitly: Record model versions, prompts, retrieved sources, tool calls and approvals. Require human authorization for actions that can affect production, safety or sensitive configurations.
How to interpret a red-team result
| Decision question | Evidence to collect | Operational implication |
|---|---|---|
| Could the AI produce an unsafe recommendation? | Prompts, source data, model output, confidence or uncertainty cues, and reviewer decision | Improve grounding, approval gates, operator training or remove the use case |
| Could an attacker reach the AI or its data? | Identity path, network route, exposed interface, permissions and logs | Segment, reduce privileges, harden access and add monitoring |
| Would defenders notice misuse? | Alert generation, telemetry coverage, investigation time and escalation records | Add network and identity detections and rehearse response |
| Could the process remain safe? | Fallback mode, manual procedure, safe-state behavior and stop evidence | Revise operating procedures and constrain automation |
| Can the finding be generalized? | Test scope, asset types, environment, assumptions and repeatability | Limit claims to the tested environment until additional evidence exists |
A successful test is not proof that the system is secure. It means only that the tested scenarios did not produce the defined failure, or that controls detected and contained them. Conversely, a failure should be prioritized by safety impact, operational availability, detection coverage, segmentation, response readiness and the feasibility of a safe retest.
Practical questions to ask before approving an exercise
- Who owns the process and has authority to stop the test?
- Which assets, accounts, prompts, data sources and tools are in scope?
- What actions are prohibited because they could affect safety, availability or vendor support?
- What synthetic or lab environment can reproduce the relevant integrations?
- How will operators, defenders and emergency contacts be notified?
- What telemetry will be captured, and how will timestamps be synchronized?
- What is the safe fallback if the AI service, network or identity provider fails?
- How will findings distinguish confirmed behavior from a theoretical consequence?
- Who owns remediation, and when will the control be retested?
The defensible conclusion
AI red teaming reveals where an AI system can be manipulated, mislead operators or cross an authority boundary. Conventional cyber red teaming reveals whether an organization can detect and contain adversary activity. In an ICS environment, both perspectives matter, but neither automatically proves a vulnerability in a live controller or an entire industry.
The strongest program connects the two without confusing them: test AI behavior in controlled conditions, test network and response controls with authorized adversary emulation, preserve OT safety and availability throughout, and report exactly what the evidence supports. That approach turns concern about AI-enabled critical-infrastructure risk into decisions an operator can verify and improve.
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