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AI red teaming has not been shown by the available evidence to uncover a specific vulnerability in a live industrial controller. What it does reveal is where an AI system could behave unsafely, be misused, or weaken an operator’s decisions. When that system is connected to operational technology (OT), those weaknesses can affect physical processes, availability, reliability, and safety.
The strongest ICS lesson comes from conventional cyber red-team assessments: organizations can have security tools and still fail to notice, interpret, or contain an adversary’s activity. The useful approach is therefore layered—test the AI, test the surrounding IT and OT defenses, and test the people and recovery procedures—without confusing any one assessment with proof about every industrial facility.
What the phrase “AI red teaming” means in an ICS context
Red teaming is an evaluation method, not a guarantee of safety. Its findings are limited by the system, environment, permissions, techniques, and actions included in the exercise. In an industrial setting, that boundary is especially important because OT systems directly monitor or change physical equipment.
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By contrast, a conventional cyber red team emulates an adversary against an organization. It may test identity controls, network segmentation, monitoring, incident response, and the ability of staff to recognize and contain an intrusion. The two forms of testing overlap in their adversarial mindset, but they answer different questions.
| Axis | AI-system red teaming | Conventional cyber red teaming |
|---|---|---|
| Object under test | AI model or AI-enabled system | Organization’s people, processes, networks, and security controls |
| Test aim | Identify AI flaws, misuse, or undesirable behavior | Emulate adversary activity and assess detection and response |
| Evidence available here | CISA’s AI roadmap and NIST’s ARIA program | CISA critical-infrastructure assessment advisories |
| ICS-specific conclusion | No direct ICS test result is established | Findings illustrate organizational defense gaps, not universal ICS weaknesses |
What AI can change in critical-infrastructure risk
CISA’s 2023–2024 AI roadmap treats AI security as both the security of AI-enabled systems and the use of AI in cybersecurity. It identifies a need to assess AI adoption in critical-infrastructure sectors because deployment could increase exposure to critical failures, physical attacks, or cyberattacks. The roadmap also calls for adapting guidance and services, including best practices for AI red teaming.
That statement is a risk focus and a planning commitment, not a report that a particular AI red-team exercise compromised an industrial control system. It supports asking structured questions before deployment:
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- Could an AI system give an operator a confident but incorrect explanation of a process alarm?
- Could a connected assistant expose sensitive diagrams, credentials, or maintenance information through an overly broad data connection?
- Could an automated recommendation cause a change to a control process without the review, interlock, or approval that the process requires?
- Can defenders tell whether unusual behavior came from an adversary, a model update, bad data, or a normal process change?
These are threat and assurance questions. They should be tested in a controlled environment rather than treated as documented incidents at a specific facility.
What CISA’s conventional red-team assessments actually found
The 2024 assessment: access, movement, and delayed action
In an advisory dated November 21, 2024, CISA described an assessment requested by a U.S. critical-infrastructure organization. The red team gained initial access through a web shell left by a third party’s earlier security assessment. From there, it moved through the demilitarized zone (DMZ) into the organization’s network, compromised the domain, and reached sensitive business systems.
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The organization saw 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 (EDR) without enough network-layer protection. Those findings show why a security program must test whether alerts are noticed, investigated, escalated, and contained—not merely whether a product is installed.
The advisory does not describe an AI red-team exercise, a PLC exploit, or a test of an industrial process. It is evidence about the assessed organization’s defenses and response. A different facility may have different architecture, controls, staffing, and risk tolerance.
The 2023 assessment: persistence across separated sites
A separate CISA advisory covering a 2022 assessment at a large critical-infrastructure organization with geographically separated sites reported persistent access and lateral movement. CISA recommended monitoring, regular assessment, and testing to ensure that security processes can detect and mitigate adversary activity.
Again, this was conventional cyber testing. It should not be recast as evidence that an AI system attacked an ICS or that every geographically distributed operator has the same weakness. Its value is methodological: assumed defenses need to be exercised under realistic conditions.
Why OT and ICS require a different risk calculus
NIST defines OT as programmable systems or devices that interact with the physical environment by directly monitoring or changing devices, processes, or events. Examples include industrial control systems, building automation, transportation systems, and physical-access systems.
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For OT, a technically successful action can still be an operational failure if it disrupts production, violates a safety limit, damages equipment, or prevents operators from seeing the true state of a process. Performance, reliability, and safety are therefore part of the security boundary. A control that is routine in an office network—such as aggressive scanning, an immediate reboot, or an unplanned software update—may be unacceptable during a continuous or safety-critical operation.
NIST’s SP 800-82 Rev. 4 page is an initial public draft published September 21, 2026, with comments due November 30, 2026. It addresses OT threats, vulnerabilities, and countermeasures while accounting for these operational constraints. The 2023 edition of SP 800-82 Rev. 3 is the final edition; the Rev. 4 document should be labeled a draft during its comment period.
Manufacturing has a related integrity challenge. The NIST SP 1800-10 executive summary explains that connecting OT and IT can expand business capabilities and remote access while creating opportunities to compromise ICS and data integrity. Its example solutions are useful for designing protections, but they are not reports of AI red-team results.
How to red-team an ICS without creating an outage
A safe exercise is an engineering and governance activity before it is a technical one. Do not probe live industrial equipment without written authorization from the asset owner, the control-system owner, safety leadership, and the organization’s incident-response authority.
- Define the objective. Choose a question such as whether a suspicious remote session is detected, whether an operator can validate an AI-generated recommendation, or whether a compromised business account can reach a control-zone asset. Do not begin with an unrestricted “find everything” brief.
- Set written boundaries. List facilities, networks, accounts, vendors, protocols, test windows, prohibited actions, and emergency contacts. Explicitly exclude safety-system changes, uncontrolled commands, denial-of-service activity, and actions that could alter a physical process unless the responsible authority has approved a safe simulation.
- Build an asset and dependency map. Identify enterprise systems, DMZ services, engineering workstations, historians, supervisory control and data acquisition (SCADA) servers, remote-access paths, controllers, safety systems, and critical third-party connections. Record which links are monitored and which are not.
- Choose a representative test environment. Prefer a lab, digital twin, staging network, or isolated replica for intrusive actions. Use passive observation and carefully controlled validation in production when a live test is unavoidable.
- Coordinate operations and safety. The control-room manager, process engineer, maintenance lead, safety officer, and security lead should agree on start and stop conditions. Define a person who can terminate the exercise immediately.
- Test in stages. Start with benign discovery and authentication scenarios, then test detection of approved simulated actions. Escalate only when the previous stage is understood and the risk owner authorizes it.
- Observe more than the attacker path. Capture network telemetry, endpoint alerts, identity events, operator notifications, physical-process alarms, ticket creation, and escalation times. A red team that reaches a target but leaves no usable evidence may reveal a monitoring gap; one that triggers an alarm no one handles reveals a response gap.
- Use explicit stop criteria. Stop for an unexpected process change, loss of control visibility, safety-alarm activation, unstable equipment behavior, unplanned account impact, or any uncertainty about whether the exercise is affecting production.
- Validate recovery. Confirm that credentials can be revoked, remote access disabled, affected hosts isolated, trusted configurations restored, and operators given accurate status information. Recovery should be tested as deliberately as initial detection.
- Report by risk and owner. For each finding, state the affected asset or process, evidence observed, safety and availability implications, owner, corrective action, and retest condition. Keep assessment-specific facts separate from sector-wide conclusions.
CISA’s ICS Recommended Practices collection provides implementation guidance on defense in depth, incident response, patch management, control-system remote access, and related topics. It is a library of practices to adapt to the facility, not a single control that makes an ICS secure.
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What an AI-focused test should add
An AI evaluation should be designed as a separate layer that connects to the ICS assurance plan only through defined interfaces. Test the model or application in a non-production environment first, then conduct a controlled field evaluation if the risk owner approves it.
Model behavior and misuse
Evaluate whether the system follows its intended operating policy, refuses unsafe or unauthorized requests, protects sensitive process information, and communicates uncertainty. Include ordinary operator language, ambiguous requests, incomplete sensor context, and conflicting instructions. The goal is to find undesirable behavior without sending uncontrolled commands to equipment.
Data and integration boundaries
Verify which historians, maintenance records, network zones, identity systems, and vendor services the AI can read or influence. Test whether a user can retrieve information outside their role, whether stale or corrupted data is identified, and whether a model update changes behavior. Every action that could affect a process should have a separate authorization and audit trail.
Human decisions and fallback
Measure whether operators can distinguish a recommendation from a confirmed process fact, whether a qualified person must approve consequential actions, and whether the plant can continue safely when the AI is unavailable. A useful red-team finding may be a confusing explanation or an unclear handoff rather than a software exploit.
ARIA’s model-testing, red-teaming, and field-testing distinction helps keep these activities ordered: establish model behavior, challenge it adversarially, then evaluate it in the operational context with appropriate safeguards.
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What to measure after the exercise
There is no universal score that converts an ICS red-team result into a sector-wide risk rating. Use measures that match the exercise objective and record the conditions under which they were obtained.
- Detection: Was the activity logged at the network, identity, endpoint, application, and process layers? How long until someone recognized it?
- Validation: Could staff distinguish an authorized test, a model error, a maintenance event, and an intrusion?
- Containment: Could the organization isolate a path or account without losing essential control visibility?
- Safety and availability: Did the test preserve required process states, alarm functions, redundancy, and operator access?
- Response: Were escalation contacts reachable, decisions documented, and incident procedures usable during the exercise?
- Recovery: Could trusted configurations, credentials, and communications be restored and verified?
- Learning: Were findings assigned to owners, given deadlines, and retested after remediation?
How to interpret the evidence without overclaiming
The evidence supports three bounded conclusions. First, AI adoption in critical infrastructure deserves explicit security evaluation because failures could have physical as well as cyber consequences. Second, conventional red teams have shown that detection and response can fail even when an organization has established security tooling. Third, OT guidance must account for performance, reliability, and safety rather than importing office-network assumptions unchanged.
It does not support the claim that AI red teaming has already demonstrated a particular ICS exploit. Nor does one CISA assessment prove that every plant, utility, or transportation operator has the same architecture or exposure. The responsible conclusion is a testing framework: evaluate the AI, evaluate the organization’s defenses, and evaluate the operational and safety consequences of each finding within an authorized scope.
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A practical decision framework
Use the following sequence when deciding what kind of exercise to commission:
- If the question is whether a model can be manipulated, leak information, or produce unsafe recommendations, start with AI-system red teaming and model testing.
- If the question is whether an attacker can move through identities, networks, remote access, or business systems, commission a conventional cyber red team.
- If the question is whether that activity could affect a process, add OT architecture review, passive monitoring validation, and a safety-approved exercise in a lab or controlled window.
- If the question is whether the organization can continue and recover, test communications, isolation, restoration, and operator fallback separately.
Document which question each exercise answers. That discipline prevents an AI evaluation from being mistaken for an ICS penetration test and prevents an enterprise red-team result from being presented as proof of a controller vulnerability.
Bottom line
AI red teaming reveals how an AI system might fail or be misused; conventional cyber red teaming reveals whether an organization can detect and contain an adversary. For industrial control systems, both findings must be interpreted through safety, reliability, availability, and physical-process constraints. The most defensible program tests these layers separately, connects them through controlled interfaces, and treats every result as evidence about the tested scope—not a universal verdict on industrial infrastructure.
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