• views: 118
• date: 27 August 2026
• posted by: voska89

Robust platform guardrails are needed for AI-generated code to ensure reliability and security. Explore techniques for catching unsafe changes, maintaining efficiency, and more.

Course details

What strategies do you use for securing AI-assisted development in production environments? This course covers how to map risk and establish baseline guardrails to catch failures before they escalate. Discover how to apply policy-as-code to block high-risk patterns and protect infrastructure, Kubernetes configurations, and the supply chain. Explore the essentials of making delivery reversible by default and learn how to govern exceptions without losing control. Review key topics like managing continuous feedback loops and creating a culture of continuous improvement. This course is ideal for platform engineers, DevOps engineers, and SREs aiming to enhance their operational reliability when working with AI tools. By the end of the course, you'll be more equipped to implement guardrails that reduce incidents and maintain developer velocity without compromising security. Whether you are responsible for platform workflows or interested in AI's impact on software delivery, you can benefit from the insights shared on safeguarding AI-driven development processes.

Skills covered

AI Security, Artificial Intelligence (AI), DevOps, AI-Powered Development, Platform Engineering, Continuous Improvement

Homepage

https://www.linkedin.com/learning/secure-ai-assisted-development-in-production-practical-guardrails-for-platform-and-devops-teams

   
   
   

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