Approve AI in software without approving uncontrolled data risk.
This webapp translates AI usage in engineering into board-level decisions. It explains how senior developers should operate as product managers and reviewers, what is acceptable in regulated financial services, and why every prompt is an outbound data channel.
- Green AI supports code drafts and documentation with no sensitive data.
- Amber AI may operate inside private tenants with logging, redaction, and retention controls.
- Red No client data goes to public AI services. Ever.
Senior developers lead
The most successful teams treat AI as a junior producer. Seniors define intent, constraints, and acceptance criteria, then redirect until the output is auditable.
Regulated boundaries
Banks, insurers, audit, and funds can adopt AI safely when data classification dictates the deployment model, not convenience.
Data leakage is structural
Every prompt leaves your device, creating a permanent record. Scale does not reduce risk; it makes exposure statistically visible.
Start here
Use the decision flow to classify risk, identify controls, and decide whether a use-case is allowed.
Industry → Data type → Deployment → Purpose → Decision
Launch decision flowWhat this site covers
AI Guidance Academy
Role-based tracks that combine learning paths, decision flows, and governance outputs.
Enter the academyUse-case library
Industry-specific patterns with risk ratings, deployment guidance, and controls.
Explore use casesLearning paths
Executive modules on data leakage, operating model, and audit-ready controls.
View learning pathsGovernance pack
Copy-ready policy templates, risk questionnaires, and acceptable use matrix.
Open governance packGovernance essentials
These controls are the minimum for approving AI in regulated delivery pipelines.
AI use follows data tiering, not user preference.
Redaction, retention, and logging by policy.
Every AI action is attributable and reviewable.