Article: Governing AI in the Cloud: A Practical Guide for Architects
Your AI governance strategy is still manual. Here's how to embed it into delivery pipelines instead.
Why it matters
As enterprises scale AI deployments in cloud environments, governance frameworks that balance security, compliance, and developer velocity become critical infrastructure decisions. This practical guide addresses the operational and policy layer that leadership and architects need to implement.
The key facts
12 to knowShadow AI discovery as governance starting point
Data classification at creation (shift-left approach)
IAM-based enforcement model
Policy-as-code implementation
Governance embedded in delivery pipelines vs. manual processes
Security, compliance, and developer productivity as competing priorities
Focus on shadow AI discovery as governance entry point
Data classification at creation (not post-hoc)
IAM-based enforcement for AI workloads
Policy-as-code approach to compliance
Integration with delivery pipelines to avoid manual overhead
Balance between security, compliance, and developer productivity
Go to the source
InfoQ AI/MLinfoq.com
Publisher excerpt: In this article, the author outlines a practical approach to AI governance in the cloud, covering discovery of shadow AI, data classification at creation, IAM-based enforcement, policy-as-code, and operational controls. The article shows how organizations can embed governance into delivery…