Last updated: 2026-02-10
Policy Guardrails for Enterprise AI Rollouts
A practical baseline for model governance that legal and engineering teams both accept.
Document allowed model classes by data sensitivity and business workflow.
Define retention defaults and escalation paths before production release.
Translate policy into deploy-time controls and logging requirements.
Tradeoffs and constraints
- Policy rigor improves trust but can slow experimentation.
- Broad exceptions improve delivery speed but increase audit risk.
Sources
- NIST AI RMF mappings
- Enterprise control catalogs
- Security architecture reviews