We’ve been talking about AI accelerating development, but there’s another side to consider: AI doesn’t just write code faster – it writes vulnerable code faster too.

AI models were trained on decades of code from Stack Overflow and GitHub repos, including all the bad examples. When you ask AI to “build a user login system,” it might give you something that works perfectly but stores passwords in plain text.

Traditional code reviews often miss this. Your senior developers are focused on logic errors and performance issues, not spotting security anti-patterns in AI-generated code blocks they didn’t write themselves.

The solution is being proactive with your AI interactions. Whether you’re using Claude Code with a claude.md file or AWS Q with custom rules in the .amazonq/rules folder, define your security requirements upfront. For example: “Never store passwords in plain text. Always use bcrypt or similar hashing. Include input validation for all user data. Follow OWASP guidelines for authentication.”

Treat your AI code like third-party libraries. You wouldn’t deploy external dependencies without security scanning, so why treat AI-generated code differently? Include tools like Semgrep and CodeQL directly in your CI/CD pipeline – make security scanning a required gate, not an optional review step.

The speed advantage of AI development only works if you can deploy safely. Getting this right means building security into the development process, not bolting it on afterward.

#CyberSecurity #AICodeAssistant #TechLeadership #CIO #SecureCode #DevSecOps #RiskManagement #SoftwareDevelopment