The integration of generative AI into platform engineering opens new possibilities to automate and improve the DevOps lifecycle, even in highly regulated software development environments. However, naive adoption often leads to uncontrolled generation, rising costs, privacy concerns, and low-quality outputs that increase review and compliance effort. In this talk, we present a practical, zero-trust approach to integrating generative AI into the DevOps cycle without disrupting existing processes. We show how to safely embed AI into platform engineering by combining self-hosted models for privacy and cost control with strict validation gates already familiar to DevOps teams. Starting from the DevOps lifecycle, we demonstrate how generative AI can be used to analyze and resolve code quality issues detected by tools like Sonar, generate high-quality unit tests using mutation testing techniques, and automatically validate AI-generated results through builds, tests, coverage checks, and issue resolution verification. We also discuss how to tune LLM hyperparameters to balance determinism, creativity, and cost, avoiding unnecessary generation while focusing AI efforts on high-value outcomes. Attendees will leave with concrete patterns to integrate generative AI into regulated DevOps pipelines: improving code quality and test coverage, controlling costs, preserving privacy, and keeping humans in control through measurable, automated verification rather than manual review.