AI-generated code is already in production. Whether we are comfortable with that or not is beside the point. In the OpenStack project, which I have helped steward for more than 15 years, we are seeing developers submit patches built with AI assistance, and sometimes patches composed almost entirely by AI tools. Some of those contributions have already landed in the past release cycle. This is happening in one of the most rigorously governed open source projects in the world. It is happening everywhere else, too. The code generation itself is not the problem. AI is genuinely good at producing computer programs because the structure of code is sufficiently predictable and syntactically constrained to play to the technology’s strengths. The problem is what happens next. Every AI-generated patch still needs to be reviewed for correctness, security, and long-term maintainability. And when code is easier to produce, more code gets proposed, which puts enormous pressure on the human rev...
CI/CD environments depend on far more than repositories and deployment infrastructure. Developer endpoints hold sensitive data: cloud credentials, SSH keys, deployment permissions, direct access to internal systems. Endpoint security and control are part of daily operational risk management. Engineering teams are shifting more and more toward distributed workflows, so discussions around CI/CD security include the security posture of the devices connected to the pipeline. Many organizations already focus their CI/CD security efforts on secrets management , dependency scanning and supply chain controls. However, advanced endpoint security solutions are also relevant in cloud-native development environments, where local devices maintain direct access to production workflows. Endpoint Compromise Can Bypass Mature CI/CD Controls CI/CD security discussions mostly focus on repositories, containers, infrastructure, and deployment automation. Developer endpoints are often overlooked as a par...