Shadow AI Response Guide: Steps & Checklist

Step 1: Discover

Map where AI is used without central approval. Use CASB, network and browser logs, or a dedicated Shadow AI audit (e.g. scanning for known AI app patterns). Identify business units, high-risk apps, and data exposure.

• Inventory AI tools in use (consumer apps, APIs, embedded features).
• Tag by risk: data sensitivity, regulatory impact, cost.

Step 2: Assess risk

Prioritize by data leakage, compliance (e.g. EU AI Act, sector rules), and cost. Decide which uses are acceptable with controls and which must be blocked or migrated to approved tools.

Step 3: Define policy

Write a clear AI use policy: what is allowed, what requires approval, what is prohibited. Include data handling, acceptable use, and escalation. Align with AIMO Standard, EU AI Act, ISO 42001, or NIST AI RMF as needed.

Step 4: Allowlist and request flow

Maintain an allowlist (whitelist) of approved AI tools and use cases. Define a lightweight request flow so new tools can be evaluated and approved without blocking innovation. Use a digital register instead of spreadsheets where possible.

Step 5: Monitor and iterate

Run periodic discovery and monitoring to catch new Shadow AI. Optionally use 24/365 monitoring with Human-in-the-Loop for high-risk decisions. Update the allowlist and policy as tools and regulations change.

Checklist summary

• □ Discovery completed (audit or log-based scan).
• □ Risk assessment and prioritization done.
• □ AI use policy documented and communicated.
• □ Allowlist and request flow in place.
• □ Monitoring and review cycle defined.