Your team just got approval to deploy an LLM application into production. Before you flip the switch, you need to prove you've addressed security, compliance, and operational risk. This checklist translates the three-pillar deployment framework, modular inference, guardrails, and observability, into actionable controls you can verify before go-live.
Use this to confirm you're not just deploying AI, but governing it.
What This Checklist Covers
This checklist operationalizes the infrastructure requirements for enterprise LLM deployment. It focuses on three control domains: containerized inference for operational flexibility, guardrails for behavioral constraints, and observability for transparency and audit readiness. Each item maps to a specific technical control or governance requirement you can mark complete.
Every checkbox has a clear done state and a reference to what good looks like in production.
Prerequisites
Before you start this checklist, confirm:
- Your AI Management System (per ISO/IEC 42001) includes LLM applications in scope
- You've completed an AI System Impact Assessment for this deployment
- You have documented Model Limitations and Use Restrictions
- Your model risk tiering is complete and approved
- You've identified the AI Actors responsible for deployment, monitoring, and incident response
If any prerequisite is missing, pause and complete it. Deploying without foundational governance creates technical debt you'll pay back during your first audit.
Deployment Readiness Checklist
Modular Inference Controls
1. Containerized deployment architecture is implemented
You've deployed the LLM in a containerized format that can run consistently across cloud and on-premises environments. Good looks like: your model runs in Docker or Kubernetes with version-controlled images, environment parity between staging and production, and documented rollback procedures.
2. Inference latency and throughput meet documented requirements
You've benchmarked inference performance under expected load and confirmed it meets your service-level objectives. Good looks like: latency percentiles (p50, p95, p99) documented in your Technical Documentation (Annex IV), load testing results stored as Validation Evidence, and capacity planning tied to business requirements.
3. Data residency and sovereignty controls are enforced
Your deployment architecture ensures sensitive data never crosses jurisdictional boundaries prohibited by GDPR or contractual terms. Good looks like: network policies that prevent data egress, documented data flow diagrams showing where inference occurs, and configuration that locks models to specific geographic regions.
4. Model versioning and rollback capability is verified
You can identify which model version is running at any time and revert to a previous version without data loss. Good looks like: semantic versioning for all model artifacts, automated rollback scripts tested in staging, and change logs that tie model versions to deployment timestamps.
Guardrail Implementation
5. Input validation rules are active and tested
You've implemented guardrails that block or flag problematic user inputs before they reach the model. Good looks like: rules that detect prompt injection attempts, PII in user queries, or prohibited topics, with test cases proving each rule fires correctly.
6. Output filtering prevents sensitive disclosures
Your guardrails scan model responses for PII, proprietary information, or toxic content before delivery to users. Good looks like: real-time scanning with configurable sensitivity thresholds, documented handling procedures when guardrails trigger, and test cases showing blocked outputs never reach end users.
7. Behavioral constraints align with Instructions for Use
The model's allowed behaviors match what you documented in your Instructions for Use and Model Limitations. Good looks like: guardrail rules encoded in a dialogue modeling framework, constraints that prevent off-topic responses, and test scenarios confirming the model stays within defined boundaries.
8. Guardrail bypass procedures are documented and controlled
You've defined who can override guardrails, under what conditions, and how those overrides are logged. Good looks like: role-based access controls for bypass authority, incident tickets required for each override, and audit logs showing who bypassed what and why.
Observability and Monitoring
9. Real-time monitoring dashboards are operational
You can see current LLM performance metrics without waiting for batch reports. Good looks like: dashboards showing request volume, latency, error rates, and guardrail trigger frequency, with alerts configured for threshold breaches.
10. Prompt and response logging captures audit evidence
Every user interaction is logged with sufficient detail to reconstruct what happened during an incident or audit. Good looks like: structured logs containing timestamps, user IDs (pseudonymized per GDPR), prompts, responses, model versions, and guardrail decisions, retained per your data retention policy.
11. Hallucination detection is enabled
You've deployed automated scoring to flag when the model generates content not grounded in source data. Good looks like: integration with observability tools that score response faithfulness, threshold-based alerts when hallucination rates spike, and documented investigation procedures.
12. Toxicity and bias monitoring is active
You're tracking whether model outputs exhibit harmful patterns across demographic groups. Good looks like: automated toxicity scoring on responses, bias metrics calculated by user segment, and monthly reports reviewed by your governance committee.
13. PII leakage detection is configured
Your monitoring system flags when the model inadvertently exposes personal data. Good looks like: pattern matching for common PII formats (SSNs, credit cards, email addresses), alerts routed to your privacy team, and procedures for Responsible Disclosure if leakage is confirmed.
14. Adversarial attack detection is deployed
You're monitoring for prompt injection, jailbreak attempts, and other adversarial inputs. Good looks like: signature-based detection for known attack patterns, anomaly detection for unusual input sequences, and integration with your security incident response workflow.
Governance and Compliance
15. Stakeholder Engagement records are complete
You've documented who reviewed this deployment, what concerns were raised, and how you addressed them. Good looks like: meeting notes from legal, compliance, security, and business stakeholders; a decision log showing risk acceptance; and sign-off from your AI governance committee.
16. Post-Market Monitoring plan is approved and active
You've defined what you'll track after launch and who's responsible for reviewing it. Good looks like: a monitoring plan specifying metrics, thresholds, review frequency, and escalation paths, aligned with ISO/IEC 42001 Annex A controls and your AI RMF Profile.
17. Incident response procedures are tested
Your team knows what to do when monitoring detects a problem. Good looks like: a runbook covering common failure modes, tabletop exercises proving your team can execute the runbook, and integration with your broader incident management system.
Common Mistakes
Treating observability as optional. Teams deploy with monitoring "on the roadmap." Your first audit will ask for logs you don't have. Observability is your proof of control.
Configuring guardrails too loosely. You set permissive thresholds to avoid blocking legitimate use. Then your model leaks PII in production. Start strict and loosen based on evidence.
Skipping containerization because "we're just testing." Your test deployment becomes production without architecture changes. Now you can't enforce data residency or roll back cleanly. Modular deployment isn't a production-only concern.
Logging prompts but not responses. You capture what users asked but not what the model said. During an incident, you can't reconstruct the full interaction. Log both or log neither.
Next Steps
If you checked every box, you're ready to deploy with defensible controls. Schedule your go-live and confirm your Post-Market Monitoring reviews are calendared.
If you have gaps, prioritize them by risk. Missing guardrails or observability creates immediate compliance exposure. Missing containerization creates technical debt but won't trigger regulatory findings on day one.
After deployment, your governance work continues. Review your monitoring dashboards weekly for the first month, then align to your Post-Market Monitoring cadence. Update your Model Limitations and Use Restrictions as you learn what works and what doesn't. And when regulations change, because they will, revisit this checklist to confirm your controls still map to current requirements.
You're not deploying AI. You're deploying governed AI. This checklist is how you prove the difference.
