Security Checklist for AI-Generated Apps: Auth, RBAC, Payments
Whether you're shipping a survey app builder AI, a dashboard builder AI, or standing up a CI/CD setup for AI-generated projects, security must be designed in, not bolted on. Use this opinionated checklist to reduce breach blast radius while keeping iteration speed high.
Authentication
- Enforce SSO via OIDC/SAML; require WebAuthn for admins and support accounts.
- Use short-lived JWTs with audience checks; store refresh tokens in httpOnly, sameSite=strict cookies.
- Enable adaptive MFA on risky signals (new device, TOR, geo-velocity).
- Protect logins with rate limits and bot detection; block credential stuffing via breached password checks.
- For service-to-service calls, require mTLS or workload identity; avoid long-lived API keys.
- Implement user-consented "support impersonation" with automatic audit trails and time boxing.
RBAC and Data Boundaries
- Model roles explicitly: owner, builder, reviewer, respondent, finance; deny by default.
- Scope data access by tenant and project; use row-level security for per-survey isolation.
- Pass only minimal attributes to prompts; redact PII before model calls and rehydrate post-response.
- Sandbox LLM tools; maintain allowlists for connectors, SQL schemas, and file types.
- Validate AI-generated SQL against a policy engine; block full table scans and write operations.
- Generate signed, expiring share links; disable public indexes for private dashboards.
Payments and Entitlements
- Tokenize cards with a PCI-compliant gateway; never store PANs.
- Use idempotency keys on charge and subscription endpoints; verify webhook signatures and replay windows.
- Gate features via server-side entitlements, not client flags (e.g., export, premium models).
- Run 3DS or SCA where required; add velocity checks and BIN risk scoring for fraud.
- Reconcile invoices to events; lock resource quotas to plan changes in real time.
CI/CD and Runtime Guardrails
- Harden a CI/CD setup for AI-generated projects: signed builds (Sigstore), SBOMs, and dependency pinning.
- Scan prompts, code, and configs for secrets; block on policy violations.
- Version datasets, prompts, and model IDs; record lineage for rollbacks and audits.
- Use runtime egress proxies to restrict LLM endpoints and redact sensitive fields.
- Apply WAF, RASP, and per-tenant rate limits; enable CSP and strict transport security.
Monitoring, Privacy, and Response
- Log every privilege change and AI action with actor, input hash, and output size.
- Alert on abnormal token spend, query patterns, or data export bursts.
- Offer data retention controls; support DP or k-anonymity for survey analytics.
- Practice incident drills: rotate keys, revoke sessions, and communicate within SLAs.
Before launch, run threat modeling per feature, ship secure defaults, and test abuse cases with red-team prompts. For a survey app builder AI or a dashboard builder AI, create tenant seed data in staging, then destroy it automatically. Finally, codify this checklist in your CI/CD setup for AI-generated projects so every commit revalidates auth, RBAC, payments, and observability. Backups, keys, and tokens rotated regularly.
