MLOps for regulated industries: what’s different

Standard MLOps playbooks are written for teams shipping consumer AI. Regulated industries need a different discipline. The model is the same; the paperwork around it isn’t.

Five controls your auditor will test

1. Reproducibility. Can you recreate any production model from scratch using artifacts still in your system? If not, your change-management policy has a hole.
2. Approval trail. Every production promotion needs a named human approver and a timestamp. "CI passed" is not an approval.
3. Dataset lineage. Every training run tied to a dataset version tied to a source system. Auditors will trace backwards from a prediction to the raw data point.
4. Evaluation evidence. Evals run before promotion, with results stored next to the model version, not in an ephemeral CI log.
5. Rollback capability. Previous version promotable back to production in under an hour without redeployment.

The reference stack we build

• Source control for all code, configs, and notebooks — Git, with signed commits where policy requires
• Data versioning via DVC, LakeFS, or a lakehouse with time-travel
• Experiment tracking with MLflow or Weights & Biases (self-hosted for sensitive deployments)
• Feature store where features are shared across models (Feast, Tecton)
• Model registry as the single source of truth for production models
• CI/CD with explicit promotion gates (not auto-deploy)
• Observability via Prometheus + Grafana (metrics) and a drift-detection service
• Audit log for every registry state change, fed to SIEM

Processes that matter more than tools

The stack is table-stakes. The processes around it are what auditors actually examine:

• Model cards for every deployed model — purpose, training data, known limitations, fairness analysis. Written in plain English.
• Change-advisory-board (CAB) review for significant model updates. Low-risk tweaks can go through a lightweight process; major changes (new architecture, new data source) go through CAB.
• Drift-detection thresholds defined before launch, not reverse-engineered when something breaks. Published to the monitoring dashboard.
• Incident-response playbook with AI-specific scenarios: model malfunction, data leakage via output, hallucination at scale.
• Periodic revalidation — every model is re-evaluated against its golden set at a defined cadence (quarterly is common).

Mistakes that fail audits

• No named owner for a production model. "The team owns it" is not an answer.
• Training data accessed directly from a production warehouse without version capture.
• Evaluation scripts living in notebooks instead of version-controlled modules.
• Promotion done by whoever has admin on the registry. No approver record.
• "Monitoring" that amounts to someone eyeballing a dashboard monthly.

Where to start if you’re behind

Most teams we engage have 20-40% of this in place. The priority order we recommend:

1. Model registry with approval gates (week 1-2)
2. Evaluation harness running on a schedule (week 2-3)
3. Dataset versioning tied to experiment runs (week 3-4)
4. Drift detection with alerting (week 4-6)
5. Model cards and change-advisory process (week 6-8)
6. Audit-log feed to SIEM (week 8-10)

This is a typical Secure AI Build scope. It doesn’t invent AI for you — it makes the AI you already have defensible.