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Secure AI Development Practices: A Guide for Founders Using AI Coding Tools

By Jai Mittal, CTO ·

Secure development practices exist because code that works is not automatically code that is safe. When an AI writes your code, the same principle applies. In fact, it applies more urgently: AI tools optimize for speed and correctness, which means security configuration — rate limiting, authentication middleware, security headers, monitoring — is consistently skipped unless you specifically ask for it.

This guide covers the practices that close the gap between "the AI built it" and "it is safe to ship."

Before you start building: set your security requirements upfront

The most effective moment to add security to an AI-built app is in the initial prompt. If you tell Lovable or Bolt to "build me a user authentication system with rate limiting on login, secure HTTP headers, and Supabase row-level security enabled on all tables," you will get more secure code than if you describe the feature without these requirements. The AI does not add security constraints unless you specify them. Your first message is the cheapest moment to ask for them.

During development: three habits that catch most issues

Run gitleaks before every push. AI agents sometimes hardcode API keys during rapid generation. A pre-commit hook catches these before they enter git history. Install once, runs automatically forever: pip install pre-commit && pre-commit install.

Audit dependencies after every AI session. When an AI adds packages, run npm audit immediately. Do not wait. Fix high and critical issues before the next feature session.

Review every new API route for authentication. AI-generated API routes are often accessible without a valid session token. After any session that adds new endpoints, check that each one either requires authentication or is explicitly documented as public.

Before launch: the four non-negotiable checks

No matter how much you trusted the AI’s output, run these four checks before launching to real users:

  1. Secret scan your git history. Run gitleaks detect --source . on the full repository, not just the current HEAD. Keys committed in past sessions may still be in history even if removed from the current code.
  2. Run npm audit on your lockfile. npm audit --audit-level=high and fix everything it flags before launch.
  3. Run a URL-based scan on your live deploy. The free LRC scan tests your actual running app for exposed endpoints, missing headers, absent rate limiting, and missing monitoring. 30 seconds.
  4. Verify your database access controls. If you use Supabase, check the RLS status of every table in the dashboard. If you use Firebase, verify your security rules reject unauthenticated reads on user-specific data.

After launch: the monitoring minimum

Once real users are in your app:

Without these three, your first production incident will be reported by a customer, not by you.

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FAQ

What are the most important secure development practices for AI-built apps?

In order of impact: (1) secret scanning with gitleaks before every push, (2) dependency audit with npm audit after every AI session, (3) reviewing API routes for authentication, (4) URL-based scan before launch, (5) error tracking and uptime monitoring after launch. Together these cover the most common failure modes in AI-generated code.

How do I prompt an AI coding tool to write secure code?

Include security requirements in your prompt. Specify: authentication requirements for each endpoint, rate limiting thresholds, input validation expectations, and security headers. The more specific your prompt, the more secure the generated code. Example: "Add rate limiting of 5 requests per minute per IP to the login endpoint and validate that the email field matches an email regex."

What should I do if gitleaks finds a secret in my git history?

Rotate the key immediately — assume it is compromised. Removing the file from the current branch does not remove it from git history. After rotating, use git-filter-repo (or BFG Repo-Cleaner) to scrub the historical commit if the repo is public. For a private repo with controlled access, rotation is the critical step.

Is there a checklist for secure AI development?

Yes — the pre-launch checklist is: (1) gitleaks on full git history, (2) npm audit --audit-level=high, (3) free URL scan on live URL, (4) verify database RLS or Firebase security rules, (5) install Sentry and verify it captures errors. The $499 LRC audit covers all of these with specific findings and a prioritized fix list.

Research sources