LLM-Powered Data Assistant: Risk & Guardrail Checklist

Checklist owner (name & role)

Person responsible for completing this checklist

Date

YYYY-MM-DD

Data & Access Controls — Confirm these controls are in place

Data encrypted at rest and in transit

Least-privilege access enforced for model and services

Sensitive fields tokenized or pseudonymized

Row-level or tenant filters applied where needed

Raw PII or credentials not sent to model

Prompt & Injection Protections — Confirm these protections

Inputs sanitized and validated

Context window limits set to prevent unintended context leakage

System prompts and response templates are controlled/whitelisted

Handling for user-supplied documents is defined

User data separated from system prompts and secrets

Output Quality & Hallucination Mitigation — Confirm these measures

Model requires grounding sources for factual claims

Provenance and citation policy enforced

Confidence thresholds and human review for critical outputs

External tool and DB calls validated and rate-limited

Structured answer formats or templates to reduce ambiguity

Provenance, Logging & Auditing — Confirm these are enabled

Full request and response logging (redacted where appropriate)

Model and prompt versioning recorded

Responses include links or IDs to source records when possible

Access and query audit trails enabled

Log retention and deletion policy defined

Privacy & Compliance — Confirm compliance controls

Data protection impact assessment completed

Data residency/locality requirements observed

Consent and opt-out mechanisms implemented

Regulatory constraints (HIPAA, GDPR, etc.) mapped

Monitoring & Operational Controls — Define monitoring signals

Error / failure rate alerts configured

Nonsensical / off-topic response detection in place

Bias/fairness indicators monitored

Usage anomaly detection and rate limits enforced

Latency and throughput monitoring enabled

Critical alert threshold (%) — e.g., % of responses flagged to trigger review

Enter a percentage (e.g., 1 for 1%)

Human-in-the-loop for critical outputs?

Yes — human approval required

No — automated only

Conditional — based on risk/thresholds

Sensitive workflows — Does this assistant touch any of these?

Financial decisions or recommendations

Clinical or health-related guidance

Legal advice or contract generation

Safety-critical operations or instructions

Hiring, firing, disciplinary recommendations

Mitigation plan for identified risks

Describe steps, owners and timeline

Experiment & Test Plan Summary

Describe hypothesis, evaluation metrics, sample data and acceptance criteria

Pilot results & key observations

Summarize pilot findings and any incidents

Deployment decision

Approved for production

Approved with conditions (list below)

Rejected — not ready

Conditions for approval (if any)

Approver name & role

Approver signature (or electronic ID)

Approval date (YYYY-MM-DD)

Additional notes or links

Link to related docs, dashboards, tickets

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LLM-Powered Data Assistant: Risk & Guardrail Checklist

LLM-Powered Data Assistant: Risk & Guardrail Checklist

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