Editor’s Note: The following is an article written for and published in DZone’s 2026 Trend Report, Generative AI: From Prototypes to Production, Operationalizing AI at Scale.
This playbook provides a tactical framework for engineering, security, and product leaders to deploy generative AI responsibly. Safe adoption requires clear boundaries, repeatable controls, and verifiable evidence rather than case-by-case approvals. The following checklist applies to internal productivity tools, customer-facing features, and custom LLM-integrated applications. Teams should use this as a baseline gate before moving any AI use case into production and revisit the criteria quarterly to account for evolving model capabilities and regulatory expectations.
Read the original article: