Checklist

Responsible LLMOps Checklist for Analytics

An interactive, auditable checklist to evaluate LLM integrations in analytics workflows. Guides teams through suitability, data minimization, access controls, observability, validation and fallback controls, cost monitoring, governance approvals, and lifecycle signals. Records evidence, a reviewer risk score, and a final go/no-go recommendation.

Interactive Tool

Responsible LLMOps Checklist for Analytics

Responsible LLMOps Checklist

This interactive checklist helps analytics teams, data engineers, product owners, and compliance partners evaluate LLM integrations before deployment and during operation. Use it to capture evidence, surface gaps, and produce an auditable record that supports go/no-go decisions and mitigation tracking.

How to use: Complete fields with the appropriate cross-functional reviewers. Provide concise evidence in the text fields. Use the risk score and final recommendation to prioritize mitigations. Save the checklist so the record is stored with the project.

Name or identifier for the analytics workflow or product using the LLM.
Date of this review (YYYY-MM-DD)
Team or person accountable for the LLM integration.
Choose the current stage.
Consider accuracy needs, interpretability, and whether deterministic rules or simpler models would suffice.
Explain why an LLM is appropriate or what constraints apply.
Inputs are limited to necessary data and sensitive fields are redacted before sending.
Describe redaction, hashing, pseudonymization, or schema filters applied.
If yes, note classification and protections.
Includes removing internal IDs, secrets, or customer data not needed for the task.
Describe filters, regex rules, or middleware used.
Least privilege applied to APIs, keys, and model management.
List roles, groups, vault usage, or key rotation policies.
Ensure logs do not contain raw sensitive inputs unless necessary and protected.
E.g., secure logging service, retention days.
Includes schema checks, business-rule validation, and anomaly detection.
Describe human review, automated rejection, or conservative defaults.
Select the level of human oversight.
List metrics (e.g., hallucination rate, confidence score, latency, cost) and alert thresholds.
Token limits, rate limits, budget alerts, and optimization strategies.
Approximate run cost to help owners assess economic risk.
Includes contracts with model providers, DPAs, or regulatory approvals.
Describe approvals, open issues, or required mitigations.
Includes signals that trigger retraining or decommissioning.
Data pipelines, labels, schedule, and ownership.
Contact points, steps, and communications templates.
Name, role, and contact info.
Record current approval state.
Reviewer subjective risk score based on the answers.
1.0 10.0
Decision based on checklist evidence.
Actionable remediation steps, owners, and deadlines.
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