AI Security
Vercel AI Security Checklist
Use this checklist before your Vercel AI app reaches production. It covers the most common gaps we see in serverless and edge AI deployments.
Last updated: 2026-08-18
Checklist
Pre-deployment
- Move AI provider keys out of code and into Vercel environment variables.
- Verify no secrets are bundled into the client-side JavaScript.
- Rotate API keys and set calendar reminders for the next rotation.
- Run dependency scans and patch known vulnerabilities.
- Document the data flow from user input to AI API and back.
Runtime
- Authenticate every serverless and edge function that calls an AI API.
- Set strict CORS headers; do not use wildcard origins in production.
- Validate and sanitize all user input before building prompts.
- Rate-limit AI endpoints per user and per IP.
- Add output filtering to block sensitive or harmful model responses.
Data & compliance
- Encrypt sensitive data in transit and at rest.
- Set retention limits for prompts, completions, and logs.
- Identify and mask PII before it reaches third-party AI APIs.
- Choose AI providers whose data-processing terms match your compliance needs.
Monitoring & incident response
- Log every AI API call with user ID, timestamp, and outcome.
- Set alerts for rate-limit violations, cost spikes, and error patterns.
- Have a runbook for key rotation and prompt-injection incidents.
- Review logs monthly for unauthorized access or data exfiltration.
How MHCIS helps
- We review your Vercel architecture and AI data flow for security gaps.
- We harden edge functions, secrets management, and API access controls.
- We build audit logging and compliance evidence into your deployment pipeline.
Common questions
Straight answers to what regulated buyers ask first.
Related compliance
AI security work maps to the same compliance program we document on these pages.