AI and machine learning (ML) have hit the mainstream as the tools people use everyday – from making restaurant reservations to shopping online – are all powered by machine learning. In fact, according to Morgan Stanley, 56% of CIOs say that recent innovations in AI are having a direct impact on investment priorities. It’s no surprise, then, that the ML Engineer role is one of the fastest growing jobs.
What does a good DevSecOps pipeline should look like from a code security perspective? We hear this question often, and even though there are multiple answers, we’ve put together a blueprint that everybody could easily start with.
Artificial Intelligence (AI) and companion coding can help developers write software faster than ever. However, as companies look to adopt AI-powered companion coding, they must be aware of the strengths and limitations of different approaches – especially regarding code security. Watch this 4-minute video to see a developer generate insecure code with ChatGPT, find the flaw with static analysis, and secure it with Veracode Fix to quickly develop a function without writing any code.