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Build the test environment as a disconnected, resettable experiment—not as a smaller copy of production. The minimum safe design has no route to live operational assets, uses synthetic or simulated processes first, constrains every AI action through an approved control layer, records complete telemetry, and gives a named operator authority to stop and restore the exercise.
That design is a practical synthesis of OT safety guidance and representative-environment practice. It is not a dedicated standard for autonomous AI attack agents. NIST SP 800-82 Rev. 3, the final edition published in September 2023, remains the relevant finalized guide; NIST lists Rev. 4, published as an initial public draft on September 21, 2026, with comments due November 30, 2026. Treat the draft as draft guidance, not as a final requirement.
What makes an AI-driven OT test different
Operational technology (OT) includes programmable systems and devices that interact with the physical environment. A controller command can change pressure, temperature, flow, motion, chemical concentration, or electrical state. That is why an experiment that looks like an ordinary network exercise can create safety, environmental, availability, and equipment risks when it reaches a real industrial process.
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Use the following rule as the design objective: an agent may observe only the data you intentionally expose, may request only allowlisted actions, and can never send an unreviewed command to a live asset.
Start with a defensive question and a written scope
Do not begin by asking which attack the agent can perform. Begin by stating what the evaluation must teach the defenders. A clear question determines the necessary process fidelity, telemetry, duration, and stopping rules.
Write an evaluation charter
- Objective: for example, determine whether analysts detect unauthorized set-point changes, assess the quality of an AI-generated incident report, or measure how quickly a control-room team can isolate a simulated segment.
- Represented architecture: identify the supervisory control and data acquisition (SCADA) layer, engineering workstation, historian, remote-access path, safety-related functions, protocols, and process model that the lab represents.
- In-scope behavior: list the techniques, accounts, hosts, tags, and protocols the exercise may touch. Exclude everything else by default.
- Evidence to collect: define required network captures, host events, controller logs, process values, agent prompts and outputs, approvals, and operator actions.
- Unsafe states: define numerical or logical limits such as a simulated tank overflowing, a motor exceeding a modeled speed, or a safety interlock being bypassed.
- Authority: name the exercise owner, the operator who can halt it, and the person authorized to reset the environment.
- Success criteria: specify observable outcomes, such as an alert generated within a target interval or a report that identifies the affected asset and process consequence. Do not substitute an impressive narrative for a measurable result.
Map behavior to the target process
MITRE ATT&CK for ICS can organize adversary behavior, but a technique catalog is not permission to test live equipment. MITRE warns that “Logic executing in ICS has a direct effect on the physical world.” Select techniques only after mapping them to the architecture and process in your charter. A scenario copied from another plant may use different tags, trust boundaries, interlocks, or operator procedures and produce misleading conclusions.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallMITRE’s threat-informed failure-scenario approach is useful here: connect an adversary action to the specific process condition it could influence, the telemetry that should reveal it, and the defensive decision you want to evaluate. Record both the intended effect and the conditions under which the action must be blocked.
Choose the least realistic environment that can answer the question
More realism is not automatically safer or more useful. Increase fidelity only when the current environment cannot answer the defined question and when you can add engineering controls to contain the additional risk.
| Environment | Best suited to | Isolation and reset characteristics | Important limitations |
|---|---|---|---|
| Software-only simulation | Testing agent reasoning, scenario logic, detection content, and reporting workflows | Can be fully disconnected and snapshotted; reset is usually fast | May omit timing, vendor behavior, protocol quirks, and physical-process coupling |
| Simulated process with realistic interfaces | Testing SCADA views, historians, Modbus or OPC UA flows, alarms, and analyst response | Still isolated from real equipment; reset the process model and virtual hosts together | Interface realism does not prove that a real controller or process would respond identically |
| Hardware-in-the-loop | Questions requiring controller firmware, I/O timing, or engineering-tool behavior | Requires a dedicated lab network, controlled I/O, interlocks, and tested recovery procedures | Greater configuration effort and a larger failure surface; hardware behavior can be difficult to reproduce |
| Physical test installation | Questions that cannot be answered without real sensors, actuators, or mechanical behavior | Needs engineered safeguards, physical barriers, emergency stops, and strict utility and network separation | Highest potential consequence, maintenance burden, and reset cost; it is not justified for every evaluation |
These categories are decision aids, not a head-to-head ranking. Published examples demonstrate simulation-based ranges and software-only OT scenarios, but they do not establish a universal winner or validated safeguards for autonomous agents.
Use a reference architecture with multiple containment layers
Separate the lab from production
Place the entire experiment on dedicated hosts, switches, wireless infrastructure, and power where practical. Do not rely on a VLAN alone when an agent could issue commands at scale. Use physical separation or a separately administered virtual environment, deny-by-default firewalls, and explicit allowlists for every permitted flow.
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Divide the lab into zones
- Process zone: the simulator, virtual PLCs, emulated I/O, or dedicated physical equipment. It should accept only the protocol traffic needed for the scenario.
- Control and supervision zone: SCADA, HMI, historian, engineering workstation, and alarm services that participants are expected to defend.
- Adversary and AI zone: the agent runtime, orchestration service, test harness, and disposable attack hosts. It must not have unrestricted administrative access to the process zone.
- Observation zone: packet capture, logs, time synchronization, and immutable evidence storage. Keep collection independent from the systems being tested so an agent cannot erase its own evidence.
- Management zone: hypervisor, snapshot, identity, and reset functions. Restrict it to the exercise operators; do not expose management interfaces to the agent.
Put a command broker between the agent and OT
Never connect a general-purpose language model directly to a PLC, RTU, safety instrumented system, or engineering workstation. Expose a broker with a narrow, typed API. The broker should validate asset identity, tag name, data type, range, rate, and current process state before forwarding a request to the simulator or test hardware.
Make every action a request that can be denied, queued, or sent for human approval. Record the prompt, model version, tool call, broker decision, approver, resulting command, and process response. Apply short-lived credentials, per-exercise tokens, rate limits, and an automatic expiry time. A read-only mode should be the default.
Build the environment in controlled stages
- Approve the charter. Have the process owner, OT engineer, security lead, and safety representative sign the objective, scope, stop conditions, data plan, and reset authority before deploying the agent.
- Inventory the represented system. Document every virtual or physical asset, operating system, firmware or software version, protocol, address, account, exposed service, and dependency. Mark which elements are illustrative rather than production-equivalent.
- Create isolated images and networks. Build from clean media, remove production secrets, disable unnecessary services, and apply deny-by-default routing. Verify the route table and firewall policy from both the agent zone and the process zone.
- Install the process model. Begin with a deterministic simulator. Define normal operating ranges, disturbances, interlocks, alarm thresholds, and a known-good initial state. Keep the model and its configuration under version control.
- Deploy the defensive stack. Add the HMI, historian, network sensors, host logging, identity service, ticketing or case system, and time source required by the evaluation. Confirm that logs are exported to the observation zone before introducing adversary behavior.
- Add the broker and agent in read-only mode. Test parsing, tool authorization, prompt handling, and refusal behavior with benign requests. Confirm that malformed asset names, out-of-range values, rapid calls, and attempts to access management interfaces are rejected.
- Run a baseline. Operate the process without an adversary and record normal network traffic, alarms, tag changes, CPU load, timing, and operator actions. Without a baseline, defenders cannot distinguish an attack effect from simulator noise.
- Exercise one behavior at a time. Start with a bounded technique and a single process consequence. Use synthetic accounts and disposable hosts. Increase duration or complexity only after the first run completes without violating a stop condition.
- Test the halt path. During a planned drill, the operator should be able to revoke the agent token, disable the broker, isolate the adversary zone, freeze the process model, and restore a snapshot. Measure whether evidence remains available after the halt.
- Reset and reconcile. Revert all virtual machines, databases, controller projects, credentials, and network policies. Compare the post-run inventory with the pre-run inventory and document any drift before another exercise.
Design AI-specific guardrails
Constrain what the model can see
Expose only synthetic identifiers, bounded tag sets, and the telemetry needed for the question. Redact passwords, certificates, personal data, vendor support details, and production topology. If the objective is detection, the agent may need event streams but not write access to process variables.
Constrain what it can do
- Use an explicit allowlist of tools and parameters; deny arbitrary shell, script, file-transfer, and network-scan functions unless they are the subject of the exercise.
- Require schema validation for every tool call and reject unknown fields.
- Set per-action limits for value ranges, call frequency, affected assets, and cumulative process change.
- Require two-person approval for actions that alter a modeled process state or disable a control.
- Prevent the agent from changing its own prompts, policies, broker rules, logging, snapshots, or credentials.
- Stop the run on repeated refusal bypasses, unexpected tool selection, policy violations, or divergence from the scenario.
Account for model uncertainty
Log the exact model identifier, configuration, system instructions, tools, temperature or equivalent sampling settings, and input data for each run. Repeat scenarios when nondeterminism matters, but do not describe repeated runs as a statistical safety guarantee unless you have a defined experiment and adequate evidence. Have a human review actions that depend on unit conversion, tag semantics, inferred topology, or ambiguous alarms.
Collect evidence that links cyber activity to process effect
Capture synchronized data from the network, hosts, applications, controller or simulator, and agent broker. At minimum, preserve:
- full or appropriately filtered network records for the protocols in scope;
- authentication, process-launch, configuration-change, and administrator events;
- SCADA, HMI, historian, alarm, and engineering-tool logs;
- process values, set points, interlock states, and simulator events at a documented sampling rate;
- agent prompts, responses, tool calls, broker decisions, approvals, and denials;
- operator observations, timestamps, stop commands, resets, and deviations from the run plan.
Use a trusted time source inside the lab and record clock offsets. Store evidence in an append-only or access-controlled repository outside the agent’s reach. Retain the baseline, run configuration, snapshots, and post-run inventory with the evidence so another team can reproduce the conditions.
Define stop, recovery, and emergency procedures before the first attack
Write stop conditions in operational terms, not just “stop if unsafe.” Examples include a process variable crossing its modeled limit, an unexpected asset receiving traffic, loss of logging, an agent attempting an unapproved tool, a safety interlock entering an unknown state, or an operator losing visibility of the HMI.
The stop procedure should identify who issues the command and the exact sequence: revoke agent access, disable the broker, block the adversary zone, freeze or place the process model in its safe state, preserve volatile evidence, and notify the exercise owner. For hardware-in-the-loop or physical equipment, use independently tested interlocks and emergency stops; do not depend on the AI software to recognize danger.
Recovery is complete only after you verify restored images, controller projects, databases, credentials, firewall rules, time settings, and monitoring. A snapshot restore is not sufficient if an external identity system, shared storage, or physical I/O retains state.
Validate security tools in a representative environment
Before changing a production OT asset, reproduce the relevant operating conditions in the lab and validate the product or behavior there. MITRE’s mitigation M0949 states: “Within industrial control environments, antivirus/antimalware installations should be limited to assets that are not involved in critical or real-time operations. To minimize the impact to system availability, all products should first be validated within a representative test environment before deployment to production systems.”
The same principle applies to agent policies, detection rules, protocol parsers, endpoint controls, and update packages. Test installation, normal operation, alert volume, CPU and memory impact, communications, rollback, and recovery. A representative environment is evidence about the tested configuration; it is not proof that every production variant will behave identically.
Use a decision matrix before adding hardware
| Question | If “yes,” the next level of fidelity may be justified | If “no,” remain at the simpler level |
|---|---|---|
| Does the question depend on real controller firmware, I/O timing, or engineering-tool behavior? | Consider hardware-in-the-loop with dedicated interlocks and reset procedures. | Use a simulator or virtual controller. |
| Is the goal detection, reporting, or analyst workflow rather than physical response? | Use realistic telemetry and interfaces without physical equipment. | Do not add hardware merely for appearance. |
| Can the added equipment be isolated, monitored, and restored by the assigned team? | Proceed only after a documented containment and recovery test. | Reduce scope or return to software simulation. |
| Can you state what the current model fails to reproduce? | Document the gap and decide whether it affects the conclusion. | Do not claim production equivalence. |
| Will the new realism change the defensive decision? | Define the decision and acceptance criteria before procurement or integration. | Keep the current environment and avoid unnecessary complexity. |
Learn from published examples without overclaiming
The ICSrange paper by Vincenzo Giuliano and Valerio Formicola (2019) describes a simulation-based cyber range that virtualizes an enterprise network connected to ICS and simulated processes, including a multi-stage exercise using a simulated water-tank environment. It is a research example, not evidence of a current commercial offering or a universal topology.
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The 2026 ICSSPulse preprint by Michail Takaronis, Athanasia Kollarou, Vyron Kampourakis, Vasileios Gkioulos, and Sokratis Katsikas describes an LLM-assisted reporting module evaluated with synthetic Modbus servers, a Factory I/O water-treatment scenario, and a custom OPC UA production-line model. The authors identify software-only, unauthenticated testbeds as constraints on performance and experimental fidelity and describe the platform as suited to small- and medium-scale laboratory environments. This supports a reporting use case; it does not demonstrate that autonomous agents can safely attack realistic OT systems.
Common design failures and fixes
| Failure | Why it is dangerous or misleading | Correction |
|---|---|---|
| Reusing production credentials or VPN profiles | A mistaken tool call can cross the lab boundary or expose secrets. | Create disposable identities, remove cached credentials, and verify access from clean images. |
| Relying on a single firewall or VLAN | One policy error or compromised administrator path can defeat the boundary. | Layer physical or separately administered separation, deny-by-default rules, and route verification. |
| Giving the model unrestricted shell or network access | The agent can escape the scenario, alter evidence, or affect management systems. | Use a typed broker, allowlisted tools, short-lived tokens, and human approval. |
| Skipping a normal-operation baseline | Analysts cannot tell attack effects from simulator or process noise. | Record stable normal runs and known alarm behavior first. |
| Testing several techniques at once | You cannot attribute an alert or process change to one behavior. | Start with one bounded action and add complexity only after controlled runs. |
| Assuming a realistic HMI proves process realism | Visual similarity can hide missing timing, interlocks, and physical dynamics. | Document model assumptions and validate only the claims the environment supports. |
| Failing to test reset and evidence preservation | An exercise may leave drift or destroy the data needed to analyze it. | Drill token revocation, isolation, snapshot restore, inventory reconciliation, and evidence retention. |
| Installing defensive software directly on critical live assets | Scanning or updates can affect availability or real-time operation. | Validate the configuration in a representative environment first; deploy to production only through the site’s change process. |
Operator checklist
- Written objective, architecture representation, scope, and success criteria approved.
- No network, identity, storage, remote-access, or physical path to live operational assets.
- Disposable accounts, clean images, deny-by-default routing, and verified firewall policy.
- Process model with documented limits, interlocks, initial state, and reset method.
- Agent connected only through a constrained broker; read-only by default.
- Allowlisted tools, parameter validation, rate limits, approval gates, and token expiry.
- Independent monitoring and immutable or access-controlled evidence storage.
- Baseline run completed and clocks synchronized.
- Named stop authority, tested halt path, emergency procedures, and recovery owner.
- Post-run inventory, configuration, credential, and snapshot reconciliation.
- Explicit statement of what the lab does not reproduce and which conclusions therefore remain limited.
What the evidence can and cannot establish
NIST’s OT guidance emphasizes safety, reliability, and performance requirements that differ from ordinary information systems. Its final Rev. 3 guide is the stable reference today; the Rev. 4 page describes broader sector coverage, alignment with NIST Cybersecurity Framework 2.0, and expanded discussion of asset management, monitoring and detection, and security architecture, but the September 2026 version remains an initial public draft.
There is no established, dedicated safety standard in the cited material for autonomous AI attack agents, no controlled comparison proving that one of the four environment types is universally safest, and no measured safety percentage or attack success rate to apply across plants. State those limits in reports. A credible conclusion says exactly which simulated architecture, process assumptions, agent configuration, and defensive question were tested.
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