Are you a software developer using artificial intelligence (AI) to generate code or create test scripts?

If so, it’s probably speeding up your work and increasing productivity, but did you know it’s also introducing risks?

Understanding the risks

AI tools are trained using vast amounts of data and where that data is ambiguous, they often apply probability logic to predict the correct answer.

The quality of the output can vary as a result. For software developers, that means there’s the potential for generating insecure or incorrect code while creating a false sense of accuracy.

Because it requires you to input data, using AI without proper safeguards also puts your sensitive or proprietary information at risk and introduces possible licensing and compliance problems.

But don’t despair, clear boundaries and maintaining human oversight mean you can still get the benefits of using AI without compromising security, compliance or code quality.

Be cautious about what you share

Keeping your sensitive or proprietary data safe is paramount. Don’t share it with an AI tool, especially cloud-hosted models, because anything you input potentially trains the model and feeds other people’s outputs.

Avoid sharing:

  • API keys, passwords, or secrets
  • Customer or user information
  • Proprietary algorithms or internal business logic
  • Source code covered by strict IP or regulatory requirements

Don’t trust AI-generated code

AI can write code quickly, but you shouldn’t assume it’s correct, secure, or optimal. You should always review AI-generated code, just like you would review code from an unfamiliar developer.

Run static analysis, security scans and peer reviews before merging anything created by AI and be aware of the common risks like outdated or deprecated libraries and insecure authentication or authorisation logic.

Keep humans in the loop

AI can help software developers, but it shouldn’t replace human engineering judgement for complex decisions.

You can safely use it for explaining unfamiliar codebases, suggesting refactors or optimisations and generating test cases or documentation.

However, avoid AI and leave your expert people in charge of final architectural decisions, security-critical logic, and compliance or legal interpretations. Always ensure there’s human oversight of all AI-generated changes, especially in core systems.

Know the licensing and IP risks

AI can generate code that resembles existing open-source projects, creating licensing risks if the output is used in commercial software.

Do run license-scanning tools, establish internal guidelines for using AI, and use AI-generated patterns and ideas.

Don’t blindly copy large chunks of AI-generated code or trust what your AI tool gives you.

If licensing compliance is critical, you might need to get legal advice before deploying AI tools broadly across your business.

Control access and use within teams

Unrestricted AI use can lead to inconsistent practices and increased risk.

Large businesses should train developers on using AI safely and have clear policies for which tools to use, how to use them and what data can be shared.

Very large organisations should consider centralising or hosting their own AI solutions for better control.

Don’t get caught out

You don’t need to miss out on harnessing the potential of AI. By being aware of the risk and following our guidance you can make AI work for your business, without exposing it to unnecessary risk.

Want to know more and find out how our policies will protect you? Contact our friendly experts today