Using AI for Secure Code: Best Practices for Developers
AI is transforming software development, accelerating workflows and enhancing efficiency. But while AI tools can automate tasks and generate code, using them responsibly is crucial for ensuring secure, high-quality software. Here’s a guide on making AI a valuable coding partner without compromising security or performance.

1. Validate AI Code Suggestions Against Trusted Sources 🛠️
AI-generated code should be treated as a draft, not the final answer. AI models may suggest outdated methods or overlook secure practices. Cross-check AI outputs with resources like official documentation, Stack Overflow, or GitHub discussions to confirm their accuracy.
Common Mistake: Relying solely on AI can lead to vulnerabilities. For instance, if AI suggests using pickle
for serialization, cross-check since pickle
can be risky for untrusted data. Safer options include json
or yaml
.
2. Write Clear, Context-Aware Prompts 🎯
The more specific the prompt, the better the AI output. Vague prompts can lead to generic, less secure code. For example, instead of “Write a login function,” try, “Generate a Python login function with bcrypt for password hashing and JWT for session management.”
Best Practice: Include security requirements and functionality details in prompts to guide AI toward generating secure code.
3. Conduct Comprehensive Testing: Unit, Integration, Security, and Performance ✅
No AI-generated code is production-ready without testing. Testing ensures the code is functional, secure, and efficient. Testing steps include:
- Unit Testing to verify function behavior.
- Integration Testing to confirm component compatibility.
- Security Testing to catch vulnerabilities.
- Performance Testing to check for efficiency under load.
At ThamesTech AI, AI-generated code undergoes these tests in our CI/CD pipeline, maintaining high standards.
Common Mistake: Skipping security or performance tests under time pressure. Integrate these into your CI/CD pipeline to ensure consistency.
4. Perform Security and Performance Checks 🔒⚙️
Even if AI-generated code seems correct, it may have security flaws or performance issues. Adding security measures like encryption and role-based access control protects against threats, while performance tests confirm efficiency.
Optimization Tip: Use tools like OWASP ZAP for security and Apache JMeter for performance testing to catch issues early.
Example Scenario: An AI-generated data retrieval function might lack pagination, resulting in slow performance. Performance testing catches this, allowing for improvements.
5. Treat AI as a Partner, Not a Replacement 👥
AI is a powerful assistant, not a substitute for skilled developers. While AI can help with repetitive tasks, experienced developers add critical insight for high-quality, secure software.
At ThamesTech AI, we use AI to enhance productivity, but human oversight ensures code quality.
Example: AI might generate a basic authentication system, but experienced developers will know to add layered security practices like session management.
Conclusion: Building Secure, Reliable Code with AI
AI can accelerate software development, but responsible use is key. By validating suggestions, writing clear prompts, conducting thorough tests, and reinforcing security, developers can harness AI’s benefits without compromising code quality. For more insights on secure, AI-driven coding practices, check out the ThamesTech AI blog.