Software Engineering

Security vulnerability scanning in code review

AI is prompted as a security-focused code auditor to identify vulnerabilities such as injection risks, auth flaws, sensitive data exposure, hardcoded secrets, and input validation gaps, then propose safer replacements.

Why the human is still essential here

A human engineer still validates whether the reported vulnerability is real, assesses risk in the actual system, and implements the secure fix responsibly.

How people use this

Injection and auth audit

AI reviews changed backend code for SQL injection risks, broken authorization checks, and unsafe input handling before the code is merged.

Snyk Code / Semgrep

Secret exposure detection

Security scanning flags hardcoded API keys, credentials, and accidental sensitive data leaks in pull requests and repositories for immediate remediation.

GitHub Advanced Security / GitGuardian

Secure fix recommendations

After identifying a vulnerability, AI suggests safer replacement patterns such as parameterized queries, stricter validation, or managed secret storage for engineer review.

GitHub Advanced Security / GitHub Copilot

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Latest community stories (1)

Personal Story

Blog

How I Use AI as a Senior Engineer

I've been using AI for code reviews for over a year. In that time I've learned one uncomfortable truth:

Most developers are using AI wrong for code reviews.

They paste code and ask "is this good?" They get back a wall of generic feedback that could apply to literally any codebase. It feels useful for about 10 seconds, then you realize nothing actionable came out of it.

The problem isn't the AI. It's the prompt.

After hundreds of iterations, I've identified the patterns that separate a mediocre AI code review from one that actually finds bugs, catches security holes, and suggests fixes a senior engineer would be proud of.

Here's what I learned — and the exact prompts I now use daily.

KengineeringSenior Engineer

May 25, 2026