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Do not treat an AI prompt as a harmless chat. Treat it as a governed data flow: identify the information, the model and provider, every connector or agent, where prompts and files are processed and retained, and who can retrieve the result. Permit only approved tools and use cases, minimize what leaves your environment, enforce least privilege and logging, and keep a human authorization boundary around anything that could affect production, safety or plant configuration.
Why industrial AI use needs its own security decision
Industrial organizations are experimenting with generative AI for maintenance summaries, engineering assistance, document search, quality analysis and code support. Those uses can expose information that is more consequential than an ordinary office document: process recipes, equipment configurations, production schedules, network diagrams, vulnerability details, credentials, supplier terms and data that reveals capacity or an outage.
AI does not remove the familiar confidentiality, integrity and availability risks. The National Institute of Standards and Technology (NIST) states: Security concerns of any data or information system apply to AI systems.
Its guidance covers the AI service, the software and hardware supporting it, and the data moving through the workflow. Review the NIST AI security and resilience guidance when defining your threat model.
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The risk is not limited to a prompt. Uploaded files, conversation history, retrieval indexes, plug-ins, connectors, agent actions and generated outputs can all carry sensitive information. A seemingly short question can disclose a plant name, a production line, a failure mode and a timestamp when combined with its attachment or connected data source.
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NIST reported in the abstract of its manufacturing practice guide that manufacturing was the second-most-targeted industry in 2022. The statement includes no percentage or attack count; it is a warning about exposure, not a probability for your facility. Read the SP 1800-10 publication page for the scope and date of that guide.
Start with an information and data-flow map
Before selecting a model, list what people and systems might send to it. Use your existing confidentiality, intellectual-property, export-control, privacy and safety classifications rather than inventing an AI-only scheme. Record the origin, owner, classification, permitted destination and retention requirement for each information type.
| Information example | Questions to answer before AI use | Typical handling decision |
|---|---|---|
| Recipes, set points and process parameters | Would disclosure reveal proprietary know-how or enable unsafe operation? Is the task possible with ranges or synthetic values? | Prohibit in general-purpose services; require explicit approval for a controlled, isolated workflow. |
| Equipment configurations and firmware details | Does the material expose exploitable versions, credentials or recovery paths? Who owns the configuration? | Redact identifiers and secrets; use an approved environment with strict access and retention controls. |
| Network diagrams, firewall rules and incident reports | Could the document reveal an attack path or a current weakness? Is a third-party connector involved? | Keep out of public tools; share only the minimum sanitized excerpt with authorized security personnel. |
| Maintenance records and sensor data | Do timestamps, asset IDs or worker details create privacy, security or production-capacity exposure? | Aggregate or de-identify where possible; define who may retrieve prompts and outputs. |
| Customer, supplier or employee information | What privacy, contractual, export or residency obligations apply? Has the data owner approved the use? | Use only a service and region that satisfy those obligations, or do not send the data. |
| Credentials, keys and tokens | Is any secret present in text, a file, an image, a log or an automatically retrieved record? | Never submit secrets. Revoke and rotate immediately if one is exposed. |
For every proposed use, draw the complete path: user or application, identity provider, AI interface, model, storage, retrieval index, plug-in or connector, provider personnel and subprocessors, output destination, and any system that can receive an action. Include text, documents, images, audio, code, agents and retrieval-augmented workflows. This is a practical implementation of the context and third-party-input analysis described in the NIST AI Risk Management Framework (AI RMF) 1.0 and the NIST Generative AI Profile.
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Use a decision path that produces a recorded answer, not an informal judgement by an individual employee.
- Identify the task. State the business purpose, the users, the expected output and whether the system only advises or can retrieve data, call tools or change another system.
- Classify every input and output. Apply the strictest relevant classification to a combined prompt and attachment. Treat generated summaries, code and recommendations as potentially sensitive until reviewed.
- Check the operating boundary. Determine whether the workflow touches an enterprise application, a plant network, an industrial control system (ICS), a safety instrumented function or a production decision.
- Choose the least-exposing method. Prefer a local or contractually controlled option, a redacted sample, synthetic data or an offline analysis when the task does not require live information.
- Route exceptions. Require data-owner, security, legal or privacy and operational-technology (OT) approval when a prohibited class, external transfer or production-connected action is proposed.
A public consumer chatbot should be considered unapproved for production information unless your organization has specifically assessed and authorized that exact service, account type, configuration and use case. “It is only a question” is not an approval criterion.
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Publish an acceptable-use policy employees can follow
A short policy is more effective when it names concrete behavior. At minimum, publish:
- the AI services, accounts and interfaces that are approved;
- approved use cases, such as drafting non-sensitive text or explaining sanitized code;
- prohibited classes, including credentials, unredacted network details and restricted process data;
- the approval owner and evidence required for an exception;
- rules for attachments, images, audio, source code, browser extensions, plug-ins, agents and retrieval connectors;
- the requirement to verify outputs before using them in engineering, maintenance, security or production work;
- how to report an accidental upload or a suspicious generated result.
The Generative AI Profile links acceptable-use policies and human-AI teaming guidance to reduced misuse, inappropriate repurposing and misalignment between a system and its users. Give employees a simple alternative when a task is blocked: a sanctioned internal tool, a redaction service or a review queue.
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Approval applies to a specific service and configuration, not to “AI” in the abstract. Have security, legal, privacy, procurement and OT owners review the provider’s current terms and technical behavior. Record the answer to each question and set a date for revalidation.
| Review area | Evidence to obtain | Why it matters |
|---|---|---|
| Model training | Whether prompts, files, feedback or outputs are used to train or improve a model, and whether an opt-out is contractual or merely a setting. | Training and feedback paths can expand who may process confidential content. |
| Retention and deletion | Retention periods for prompts, files, logs, backups and abuse-monitoring copies; deletion method and verification. | A “deleted” chat may not mean every derivative copy is removed. |
| Human and subprocessor access | Roles that can view content, support procedures, subprocessor list and geographic locations. | Access and jurisdiction affect confidentiality and legal obligations. |
| Data residency and transfer | Processing regions, cross-border transfer mechanism and controls for support or failover. | Residency promises often differ by plan, feature or incident process. |
| Connectors and agents | Scopes, tokens, read/write permissions, indexing behavior, isolation between tenants and emergency revocation. | A connector can expose more data than the original prompt and an agent can act on it. |
| Identity and audit | Single sign-on, multifactor authentication, role-based access, immutable logs, export formats and alerting. | You need to know who submitted, retrieved or shared information. |
| Change and incident support | Notice of model or feature changes, vulnerability reporting, breach notification, audit evidence and service-contact paths. | A safe assessment can become stale when a connector or model changes. |
NIST identifies intellectual-property, privacy and information-security risks from third-party integrations and calls for transparent risk management of third-party data used as model inputs. The exact answers above depend on the service contract and its live configuration; do not infer them from marketing language.
Minimize what leaves the plant
Send the smallest dataset that can accomplish the approved task. Remove direct identifiers, credentials, exact hostnames, precise network addresses, proprietary parameter values and unnecessary timestamps. Replace them with stable aliases, ranges, synthetic examples or aggregated values. Preserve enough context for a useful answer, but do not upload an entire historian export when five sanitized rows will do.
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- Redaction: strip names, secrets, asset identifiers and topology details before submission; inspect embedded metadata in images and documents.
- Pseudonymization: replace an asset or worker name with a token kept in an internal mapping that never leaves the organization.
- Aggregation: use daily or line-level totals instead of event-level records when individual events are not necessary.
- Synthetic or masked data: test prompts and code workflows on representative values that cannot be traced to a real plant condition.
- Output control: store results in an approved repository, apply the output’s classification, and prevent automatic forwarding to public channels.
Do not assume that an image, spreadsheet, log bundle or code snippet is safe because it contains no obvious prose. Hidden metadata, comments, embedded credentials and exact values can reveal the same sensitive facts as a written description.
Keep AI separated from production control
OT environments have performance, reliability and safety constraints that differ from office SaaS. NIST’s SP 800-82 Rev. 3 explains why security countermeasures must be evaluated against those constraints. A newer SP 800-82 Rev. 4 initial public draft was published September 21, 2026, with comments due November 30, 2026; it is a draft, not final guidance.
For an AI workflow that intersects with OT, establish an explicit boundary:
- keep experimentation and general-purpose prompting outside the control network;
- permit only narrowly scoped, authenticated data flows through a reviewed intermediary;
- use read-only replicas or exported, sanitized datasets instead of direct historian or controller access where feasible;
- separate development, test and production credentials and environments;
- define latency, availability and failure behavior before deployment;
- test updates, connectors and prompts without placing an unvalidated dependency in a control loop.
NIST SP 1800-10 is a March 16, 2022 practice guide with example solutions for a discrete manufacturing workcell and a continuous process control system. Its capabilities include behavioral anomaly detection, application allowlisting, file-integrity checking, change control, and user authentication and authorization. The guide says organizations should first assess risk and select capabilities appropriate to their manufacturing systems; it does not prescribe a universal product or architecture. See the NCCoE project page for the manufacturing context.
Apply identity, authorization and change controls
Use enterprise identity rather than shared AI accounts. Assign roles for submitting data, configuring connectors, approving use cases, reviewing outputs and administering the service. Grant each role only the repositories and actions required for its job, and remove access promptly when a person changes role.
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- Require multifactor authentication and single sign-on where supported.
- Use separate service identities for retrieval, analysis and write actions.
- Make connector permissions read-only by default; require a separate, time-limited approval for writes.
- Log prompts, files, retrieval events, tool calls, outputs, approvals and administrative changes according to your retention rules.
- Review logs for unusual volume, prohibited classifications, bulk downloads and attempts to bypass policy.
- Put model, prompt-template, connector and policy changes through normal change control, testing and rollback procedures.
- Use file-integrity monitoring and application allowlisting where the AI workflow handles scripts, binaries or configuration files.
Controls should be tested against plant availability and recovery objectives. An alerting or inspection component that introduces unacceptable latency or becomes a single point of failure is not automatically a safe control.
Require human review before consequential action
AI-generated text can be plausible while being wrong, incomplete or based on stale context. Require a qualified person to verify any result that could affect safety, production settings, maintenance priorities, access rights, incident response or plant configuration.
For an assistant or agent, write the authorization boundary before enabling it:
- Define the permitted data sources, commands and maximum scope of an action.
- Require the agent to present its evidence and proposed change to an identified approver.
- Validate syntax, safety limits, dependencies and expected effects in a test or simulation environment.
- Use a separately authenticated approval to execute a production change.
- Keep a tested rollback, manual override and fail-safe state.
- Record the prompt, model version, retrieved context, approval and resulting change.
Never let a conversational interface silently convert a suggestion into a controller write, a firewall change, a privileged account grant or a maintenance instruction. NIST’s planned critical-infrastructure AI profile is still under development; its 2026 concept note anticipates attention to legacy systems, distributed assets, deterministic behavior, explainability, graceful degradation and fail-safe operation. The concept note is not a completed requirement set, but those concerns are sensible design tests for any production-connected use.
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Monitoring should answer what was sent, by whom, through which service, to which connector, what was returned and whether the event violated policy. Combine service logs with identity, endpoint, network and data-loss-prevention signals where available. Alert on prohibited classifications, unusually large uploads, new connectors, repeated policy failures and attempts to move outputs to unsanctioned storage.
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Prepare an incident playbook before the first mistake:
- Contain: stop the upload or sharing link, revoke exposed tokens and disable the affected connector or account.
- Preserve evidence: capture timestamps, account details, prompts, attachments, recipients, service configuration and relevant logs.
- Assess exposure: identify the information, retention path, provider and subprocessor access, geographic processing and whether outputs were redistributed.
- Notify owners: involve security, the data owner, privacy or legal, OT operations and contractual contacts according to your plan.
- Remediate: request deletion where the contract and service allow it, rotate credentials, correct permissions and block the unsafe workflow.
- Learn: update classifications, training, technical controls and approval criteria; record whether the information must be treated as disclosed.
No single retention period or universal deletion procedure applies to every provider. Recheck the service when its model, ownership, retention terms, connector behavior or contract changes, and repeat the risk assessment for a materially different use.
Use NIST AI RMF to assign accountability
NIST AI RMF 1.0, released January 26, 2023, is voluntary and use-case agnostic. Its four functions give an industrial program a practical operating rhythm:
| Function | Industrial AI activity | Evidence to keep |
|---|---|---|
| Govern | Assign data, security, legal, privacy and OT owners; publish policy; define exception authority and accountability. | Policy, roles, approvals, contracts and training records. |
| Map | Describe the use case, data classifications, stakeholders, data flow, integrations, operating environment and failure consequences. | Data-flow diagram, inventory, threat model and impact assessment. |
| Measure | Test confidentiality, access, output quality, prompt-injection resistance, connector scope, availability and safe failure behavior. | Test results, acceptance criteria, logs and unresolved risks. |
| Manage | Prioritize mitigations, monitor use, handle incidents, approve changes and retire workflows that no longer meet requirements. | Risk register, alerts, incident records, change approvals and review dates. |
The AI RMF Playbook provides implementation ideas, but neither the framework nor the playbook supplies a universal vendor configuration. Adapt the controls to the information, service and plant consequence at hand.
A practical approval checklist
Approve an industrial AI workflow only when the owner can answer “yes” or document an accepted exception for each item:
- The business purpose, user population and expected output are documented.
- Every input, attachment, output and connected repository has a classification and owner.
- The exact service, account tier, model, region and enabled features have been identified.
- Training use, retention, deletion, human access, subprocessors and incident terms have been reviewed.
- Connector scopes, agent tools, tokens and write permissions are least-privilege and revocable.
- Redaction, synthetic data or aggregation is used wherever the task permits it.
- Identity, multifactor authentication, logging, alerting and change control are operating.
- OT owners have validated latency, availability, reliability, safety, segmentation and recovery behavior.
- A named person reviews outputs before any consequential decision or production change.
- Testing includes failure, rollback, stale-data, unauthorized-access and connector-compromise scenarios.
- An incident procedure covers containment, evidence, provider contact, disclosure assessment and remediation.
- A review date and change triggers are recorded so approval does not become permanent by neglect.
If the answer to a critical item is unknown, pause the use or reduce it to sanitized, non-production data until the gap is resolved. This approach lets teams gain value from AI without making an unexamined transfer of industrial knowledge or an unreviewed path into an operating plant.
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