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The gains to be had are potentially enormous, helping developers to work faster and more efficiently.
The dangers are compounded whenAI toolsdraw from outdated or flawed data, potentially replicating known vulnerabilities.
Applying the right policies and practices
Generative AI shouldnt be overlooked or avoided, however.
Businesses should treat GenAI as they would a Junior Developer, for example, and implement continuous code reviews.
Just as junior coders require constant review and mentorship, AI-generated code also requires rigorous checks before signing off.
The shift-left approach, which focuses on security testing earlier in the software development lifecycle, is also key.
Real-time security testing should be integrated directly into the developers workflow.
Education is also essential for the adoption of secure AI code assistance.
Developers should actively scrutinize AI-generated suggestions to ensure code is both secure and relevant.
While GenAI itself can introduce risks, it can also help mitigate them when paired with security-focused tools.
AI-powered security platforms can act as a security companion for AI-generated code.
These tools analyze code for vulnerabilities in real-time, flagging issues such as insecure coding practices and outdated dependencies.
This ensures that security checks run in the background as work progresses, complementing and empowering the shift-left mentality.
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The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc.
