Security Checklist for AI-Generated Apps: Auth, RBAC, Payments
AI accelerators can scaffold features fast, but they also hard-code secrets, over-permit APIs, and forget edge cases. Whether you ship a portfolio website builder AI, a CRM builder AI, or a bespoke enterprise tool, bake in the following controls as non-negotiables.
1) Identity and session safety
- Adopt OIDC with short-lived tokens, refresh rotation, and mandatory MFA; enforce PKCE for public clients.
- Set session cookies HttpOnly, Secure, SameSite=Strict; tie session to device fingerprint and IP risk score.
- Scope OAuth permissions to the smallest surface; block wildcard scopes generated by scaffolds.
- Automate JWKS key rotation and detect algorithm downgrades; fail closed on validation errors.
2) RBAC and data boundaries
- Policy-as-code (OPA/Cedar) checked in with tests; default deny and explicit allow.
- Attribute-based access for tenant, region, and data sensitivity; row-level filters at the query layer.
- Field-level redaction for PII export; mask emails and phone numbers in logs and AI prompts.
- In CRM builder AI, prevent cross-account report joins and ensure lead owner rules are enforced in analytics.
3) Payments you can trust
- Use hosted checkout and tokenization; avoid touching raw card data to stay out of PCI scope.
- Require SCA/3DS where applicable; verify amounts server-side to block client tampering.
- Make POSTs idempotent with keys; validate webhook signatures, rotate secrets, and replay-protection timestamps.
- Keep an immutable ledger and reconcile daily; alert on refund spikes and chargeback patterns.
4) Secure-by-default generation pipeline
- Seed your generator with hardened templates: CSP headers, strict CORS, TLS, and content sanitation.
- Pin dependencies, generate SBOMs, and scan with SAST/DAST; break builds on critical CVEs.
- Run policy gates that reject dangerous code patterns common in AI output (eval, wildcard SQL, weak regex).
5) Input, prompt, and output controls
- Validate inputs by allowlist and JSON Schema; reject on first failure.
- Harden prompts against injection: strict tool schemas, no system secret echoes, and test suites with adversarial strings.
- Escape output for HTML/Markdown; set CSP, frame-ancestors, and permission-policy headers on portfolio pages.
6) Performance-security coupling
- Rate-limit per tenant, user, and token; add circuit breakers and timeouts for upstream AI calls.
- Cache with access-scoped keys; never share across tenants; include role hash in cache keys.
- Do performance optimization for AI-generated code to remove N+1 queries that amplify DoS risk.
7) Observability and response
- Centralize, sign, and retain audit logs; hash chains for tamper evidence.
- Plant honeytokens in storage and prompt contexts; page on access.
- Maintain runbooks, kill-switches, and disaster recovery drills; practice with gamedays.
Before launch, run threat modeling per feature, map controls to SOC 2 and PCI, and test break-glass flows. For a portfolio website builder AI and a CRM builder AI alike, ship security playbooks, data residency configs, and a customer-visible changelog for policy updates.
