AI Governance Policy for LLM Systems

Enterprise buyers don’t want abstract governance. They want to know:

• who owns the workflow
• how changes are reviewed
• what gets logged
• how you evaluate and rollback
• how you handle sensitive data

Minimal viable controls (that don’t slow teams to a halt)

1. System inventory
   • what workflows exist, what they do, who owns them
2. Data handling
   • allowed inputs, redaction rules, retention
3. Change management
   • versioning for prompts/models/tools, approvals for high-risk changes
4. Logging + auditability
   • run IDs, stage logs, evidence bundles for key outputs
5. Evaluation
   • golden sets, regression gates, drift monitoring
6. Incident response
   • escalation path, rollback, postmortems

Download a policy template (Markdown): /tools/ai-governance-policy-template/

Related: /ai-governance-framework/

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