Checklist

Model Governance & MLOps Checklist for OT Deployments

An actionable, interactive pre-deployment and post-deployment checklist for models in OT environments. Captures validation, safety controls, monitoring metrics and thresholds, alerting and escalation, versioning and rollback procedures, access controls, and re-validation cadence so teams can deploy and operate models with clear governance and auditable evidence.

Interactive Tool

Model Governance & MLOps Checklist for OT Deployments

This interactive checklist helps teams confirm that a model is safe, validated, and governed before it is allowed to act on or recommend operational changes in an OT environment. Use it during design reviews, pre-deployment signoffs, and periodic re-validation. Save a response for auditability and to track remedial actions.

Unique name or registry ID for the model (include experiment or run ID if available).
Include semantic version or git/registry tag.
Where the model will run and make (or influence) decisions.
Person or role accountable for the model in production (name and role).
Describe what the model is permitted to do (notify only, propose setpoints, autonomously change parameters, etc.). Be explicit about prohibited actions.
Dataset ID, snapshot location, or link used for validation. Store immutable snapshot for audits.
List the metrics and numeric thresholds used to accept the model (e.g., accuracy >= 92%, latency < 150 ms, false positive rate < 2%).
If No, summarize deviations and mitigation plan in Additional Notes.
1 = negligible impact, 5 = critical safety impact (risk to people or major equipment). Use conservative rating.
1.0 10.0
Select the controls that reduce the risk of unsafe automated actions.
Choose methods to validate behavior in the live environment before full-scale operation.
Select at least the metrics that are relevant to the model's actions and safety impact.
Specify the numeric threshold for each monitored metric (e.g., accuracy < 90% triggers alert; input drift KL > 0.05).
Alerts should go to on-call operators and an escalation chain for safety-critical events.
List primary and backup contacts, paging method (SMS/phone/dispatcher), and expected response SLA.
A tested rollback or safe-stop must exist for any model permitted to act autonomously.
Explain exactly how to remove model control, restore previous model/version, or force safe-state. Include required approvals and automation steps.
Confirm model artifacts and training code are stored in a versioned registry.
Link to model registry entry, container image, or artifact storage.
Who can deploy, modify, or trigger the model in production?
Describe RBAC, approvals, and audit logging for model operations.
How often the model will be re-validated against fresh data and re-approved.
Date of last full re-validation (YYYY-MM-DD) or 'N/A' if not yet performed.
If Yes, higher safety and monitoring controls are required.
Record who approved deployment and on what date. Include required safety and operations signoffs.
Record any open issues, required mitigations, or planned experiments.
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