Architecture playbook

Technical architecture depth for private, governed enterprise AI.

Use this playbook to evaluate deployment patterns, controls by layer, and the operational design needed to make AI stable in production.

Deployment patterns diagram
Public API, private hosted, and private cloud each serve different risk and control requirements.

Control layers

LayerWhat must be controlledWhy it matters
DataIngress, egress, retrieval scope, retention, redactionData boundary assumptions collapse first under real use
Model and promptVersioning, evaluation, promotion criteria, rollbackSilent regressions create costly trust failures
RuntimeIdentity, secrets, request controls, loggingRuntime gaps become incident gaps
OperationsMonitoring, drift detection, runbooks, escalationAI becomes a production system only when owned
GovernanceApprovals, evidence artifacts, audit trail, policy mappingCompliance cannot be retrofitted after rollout