An Enterprise AI Governance Checklist for Software Teams

# Overview

# The checklist in practice The checklist covers seven governance areas. Each item defines the governance question, why it matters, and what "done" looks like in engineering terms. The article provides concrete examples for the first five areas and prescribes how they should integrate with CI/CD.

1. Model inventory and registration

Governance question: Do you know every AI model running in production? Why it matters: Models appear in microservices, third-party libraries, or notebooks that become production. You can't govern what you can't find. Done looks like: A central registry (even a YAML file in the repo) listing each model, purpose, owner, training data source, and last validation date. Make the registry a CI/CD gate: no registry entry, no deployment.

2. Data lineage documentation

Governance question: Can you trace a model's training data back to its source? Why it matters: When a model misbehaves, source and transformation history are the first diagnostics. NIST AI RMF identifies data provenance as core. Done looks like: For every model, documented lineage showing source datasets, transformations applied, data quality checks, and training date ranges. Version the lineage alongside model artifacts and embed lineage metadata at the pipeline level.

3. Bias and fairness testing

Governance question: Have you tested for bias in the outputs that matter? Why it matters: Not every model needs exhaustive fairness testing. Focus on outputs that affect people—hiring, credit, moderation, clinical recommendations. Done looks like: For human-impact outputs, quarterly fairness metrics disaggregated by relevant subgroups, A/B comparisons to a baseline, distribution analysis, and preset thresholds that trigger retraining or human review.

4. Human-in-the-loop criteria

Governance question: When does an AI output require human review before action? Why it matters: If you don't define this, the AI defaults to autonomous decisions, which may be inappropriate for high-risk outcomes. Done looks like: A documented classification of AI-driven decisions into three tiers—Tier 1 fully autonomous (low risk), Tier 2 human review on exceptions (medium risk), Tier 3 mandatory human review (high risk, irreversible outcomes).

5. Explainability requirements

Governance question: Can you explain why the model produced a specific output?

Done looks like: Domain-specific explainability artifacts tied to the model registry and request logs (the article begins this section and frames explainability as a requirement that varies by use case).

# Integrating with CI/CD Treat governance artifacts as deployment prerequisites. Examples in the checklist: registry entries required before deployment, lineage metadata flowing through pipelines, and automated fairness tests and drift checks as pipeline gates. The goal is operationalizing governance into developer workflows rather than leaving controls in policy documents.

# Getting started (practical steps) Start small and enforce a few high-impact gates: register models in a central file, add lineage metadata into the data pipeline, and require human-review tiers for decisions that affect people. Use automated tests in CI to block deployments that lack required governance artifacts.

# Bottom line