AI Governance · Risk Tiering · Review Routing
AI Model Risk Tiering and Review Routing
Intermediate LAB for routing AI use cases to security, privacy, legal, architecture, executive, or human-review paths based on risk tier and operational impact.
Overview
This LAB teaches how to move from AI intake and data classification into risk tiering and reviewer routing. Students learn how to decide whether a proposed AI workflow needs business-owner review, security review, privacy review, legal review, architecture review, executive approval, or denial by default.
Concept Deep Dives
Expand each concept when studying AI risk classification and reviewer routing.
Why does AI governance need risk tiers?
Risk tiers help teams apply consistent review requirements. A low-risk summarization workflow should not need the same review path as an AI workflow that touches customer records, employee data, payments, legal decisions, safety, or production systems.
What factors determine the risk tier?
Risk tiering should consider data classification, affected users, business impact, autonomy level, tool authority, production access, regulatory exposure, reversibility, and whether the AI can influence or execute material decisions.
What is review routing?
Review routing maps a risk tier to the required reviewer path. Depending on risk, the workflow may require business-owner review, security review, privacy review, legal review, architecture review, executive approval, or escalation.
Why does autonomy change the review path?
An AI system that only summarizes is different from one that drafts actions, submits requests, approves changes, or executes workflows. More autonomy requires stronger review, approval, and evidence controls.
What should executives understand?
Executives should understand that AI review is not a single checkbox. Review requirements should scale with data sensitivity, business impact, operational authority, and customer or regulatory exposure.
Visual Risk Tiering and Review Routing Model
Risk tiering turns AI intake and data classification into an approval or escalation path.
Example Scenario
A business team proposes an AI assistant for support operations. Students must decide whether the workflow only summarizes public help content, uses internal procedures, retrieves customer records, influences refund decisions, or can trigger operational actions. The review route changes based on those risk signals.
Student Exercise
Create a review routing decision for one proposed AI workflow.
1. Name the AI use case
2. Identify data classification
3. Identify autonomy level
4. Identify customer, employee, financial, legal, safety, compliance, or production impact
5. Assign risk tier: low, moderate, high, or restricted
6. Select required reviewers
7. Define approval, denial, or escalation path
8. Record evidence and constraintsGovernance Boundary
This course is a learning surface. It does not expose backend APIs, access enterprise systems, route real approvals, mutate production, or claim production enforcement.
Course surface = SecureTheCloud Labs
Runtime = read-only learning
Backend exposure = false
Live reviewer workflow = false
Model provider integration = false
Runtime mutation = false
Production enforcement claim = false