3 best practices for building software in the era of LLMs

AI has become integral to modern software development, significantly boosting coding speed and automating tasks such as writing test cases and summarizing documentation, with 81% of developers either using AI or planning to incorporate it soon. While AI tools enhance productivity, they also introduce potential security risks due to increased reliance on AI-generated code, which developers might trust without sufficient scrutiny. To mitigate these risks, developers are encouraged to adopt a zero-trust mindset toward AI-generated code, treating it as input from a junior developer that requires thorough review. Successful developers will combine AI's efficiency with a focus on security, and initiatives like GitLab's code review feature aim to enhance human judgment rather than replace it. Prompt engineering is vital for generating secure code, emphasizing the need for clear, security-aware instructions to AI models. Additionally, automated scanning for vulnerabilities throughout the development process is crucial, as AI-generated code can increase the attack surface, necessitating fast, accurate, and scalable security measures integrated into the developer's workflow. Platforms like GitLab provide native security scanning and AI-powered insights to help developers maintain speed without compromising security, making scanning an indispensable part of the development lifecycle.

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