Security Basics for AI-Generated Products
Table of contents
1. Treat authentication as a launch blocker
Even if a prototype relied on preview links, production requires robust identity. We implement managed auth providers with MFA and session hardening.
- • Delegate auth to providers like Clerk or Auth0 when timelines are tight.
- • Enforce passwordless or MFA flows for sensitive operations.
- • Scope roles tightly—especially for admin or billing actions.
2. Secrets belong in vaults, not repos
API keys and service credentials leaking in code is the fastest way to lose trust. We migrate secrets into dedicated vaults and rotate them regularly.
- • Use platform secret managers with audit trails.
- • Rotate API tokens on launch day to invalidate leaked copies.
- • Automate secret scanning to catch regressions.
3. Monitor the AI surface area
AI-powered features introduce new failure modes—from prompt injections to model drift. Continuous monitoring keeps you ahead of issues.
- • Log prompts, responses, and anomalies with privacy-aware tooling.
- • Alert on unusual usage spikes or failure codes.
- • Review traces weekly to refine guardrails and fallbacks.
Frequently asked questions
How do you balance security with launch speed?
We standardize on pre-built auth, secret management, and monitoring patterns so we can ship quickly without skipping critical safeguards.
Can you integrate with our existing security tooling?
Absolutely. We plug into your preferred SIEM, secret manager, or compliance workflows so the new app fits your security posture.
What happens after the security hardening is complete?
We deliver runbooks, alerting guidance, and next-step recommendations so your team can maintain and evolve protections over time.
Ready to ship your AI app?
Book a 72h launch sprint and we’ll deploy, secure, and document your AI-built product.
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