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.

DevSecOps best practices are increasingly being adopted to secure software supply chains. The challenge is finding ways to optimize these processes. Here are seven key considerations to help you adopt a successful and secure DevSecOps methodology.