Are you using AI to Python coding? Then you also have to think about safety
"AI makes it easier to write code, but more difficult to detect errors and vulnerabilities that are not obvious at first glance," says Kristian Botnen.
NITO offers new course in secure AI use in Python coding
More and more developers nowadays are using AI in Python coding. This provides a faster pace and new opportunities, but also new types of risk. That is why NITO offers the course Secure AI use in Python coding and development.
The course has been developed based on the experiences of course instructor Kristian Botnen, who sees an increasing need for expertise in the safe and controlled use of AI in development work. He points out that it is no longer enough for the code to work, and emphasizes the importance of the code also being safe, verifiable and ready for use in production.
AI provides momentum – but also new risks
AI assistants have become a natural tool in Python development and make it faster to write code, test ideas, and build solutions. At the same time, it becomes easier to overlook weaknesses. Bugs and vulnerabilities can sneak in without being immediately visible, and the code can work correctly even if it isn't.
"AI often gives good suggestions very quickly, but it's easy to rely too much on what you get back," says course instructor Kristian Botnen. Botnen believes that the problem is that the errors are not always obvious until you start testing or using the code in practice.
From writing code to controlling it
According to Botnen, when AI becomes part of the development process, the job is more about understanding and quality assuring the code than writing everything from scratch. Developers need to know what the code actually does, what dependencies it uses, and whether it handles data safely.
"The most important thing is not how fast you develop, but whether you have control over what you build.
Many of the challenges and flaws in AI-generated code are precisely that they are not obvious at first glance. The code can work well, but still contain weaknesses in validation, insecure dependencies, or handling sensitive information.
How to work more safely with AI and Python
Using AI in development is not about letting go of quality, but about combining speed with control. It requires that you set clear requirements for the tools you use, provide good context through prompts and work systematically with testing and verification.
"Good developers today must not only write code, but also be able to assess and quality assure what AI proposes. That is where the greatest value lies," says Botnen.
Learn to use AI safely in Python development
In the course Secure AI use in Python coding and development , you will learn how to use AI as development support in a controlled and secure manner. You get a practical workflow that you can take with you into your own working day – from the first prompt to finished, tested and safe code.