Can AI really write code that is secure enough for real users and real data?

AI can write authentication flows, validation, database queries, and API routes. Sometimes it produces a sensible starting point. Sometimes it copies a pattern that is incomplete for your app or unsafe in your configuration.

A successful build and a secure build are not the same thing. Security depends on secrets, permissions, database policies, authentication, dependencies, deployment settings, error handling, and how the system behaves under abuse.

A hosting platform may provide HTTPS for a configured site, but that does not protect an exposed secret, an open API route, a weak database policy, or an account that can access another user’s data.

The honest answer is: AI can help write secure code, but it cannot certify the result. You need evidence and review before real data is involved.

Before launch, check at least these areas:

1. Secrets: search for API keys, service-role keys, passwords, and tokens in source, logs, and client bundles.2. Authentication: confirm protected actions require the right user session.3. Authorization: test that one user cannot read or change another user’s data.4. Database rules: review RLS or equivalent access policies instead of trusting the UI.5. Inputs and errors: test invalid data, repeated requests, missing records, and permission failures.6. Dependencies and deployment: check versions, environment variables, logs, domains, and production settings.

Use an AI audit as one input, not as a security certificate. Ask it to show evidence, identify assumptions, and say what still needs a human or specialist review.