Trust
AI-Generated UI Security: Permissions, Data Boundaries, Audit, and Review
AI-generated UI is safest when the model is treated as a planner, not a trusted executor. The host product enforces component allowlists, server-side permissions, data minimization, action approvals, audit trails, accessibility checks, and versioned review before sensitive UI reaches users.
Quick answer
AI-generated UI security starts outside the model.
The product enforces what data the AI can use, which components it can render, which actions require approval, what gets logged, and how generated UI versions can be reviewed or rolled back.
Key requirements
- Design-system constraints
- Component allowlists
- Data-access permissions
- Accessibility validation
- Audit and review controls
- Versioned generated UI
- Developer escape hatches
- Server-side authorization
- Data minimization and masking
Security boundary
The model can propose a screen, but the host application authorizes data access and action execution. Generated output is validated against schemas and rendered by trusted components.
- No arbitrary script execution
- No client-trusted permission decisions
- No hidden unrestricted data joins
- No unreviewed high-risk actions
Audit boundary
Every generated UI run records the user, tenant, prompt, approved capabilities, rendered blocks, policy decisions, version, and action outcomes.
Review boundary
Sensitive generated UI needs approval queues, blocked states, read-only previews, rollback, and developer inspection before risky actions can execute.
FAQ
Can AI-generated UI execute actions directly?
High-risk actions should not execute directly from model output. They need server-side authorization, validation, approvals, and audit logging.
What is the biggest security risk?
The biggest risk is treating generated output as trusted code or trusted authorization. A governed system validates output and keeps permissions in the host product.
How does GovernedUI reduce data exposure?
GovernedUI constrains generation to approved capabilities, role-aware data access, masking rules, and auditable rendered output.