
Article: Governing AI in the Cloud: A Practical Guide for Architects
Quick Answer
Organizations face increased security risks from Shadow AI, with 71% of employees using unapproved AI tools.
Quick Take
Effective governance requires automatic data classification, policy-as-code tools, and collaboration across security and engineering teams to manage AI integrations in cloud environments.
Key Points
- 71% of employees use unapproved AI tools, increasing security vulnerabilities.
- Cloud Access Security Brokers (CASBs) help identify AI integrations but lack enforcement.
- Automatic data classification at creation simplifies governance over AI deployments.
- Policy-as-code tools like Open Policy Agent (OPA) help scale security rules effectively.
- Collaboration among security, engineering, and product teams is crucial for effective governance.
Source Excerpt
In this article, the author presents a practical framework for governing AI in the cloud through discovery, data classification, IAM enforcement, policy-as-code, and developer-friendly controls.
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from InfoQ AI, ML & Data Engineering
See more →Google Cloud Workbench Notebooks Extension Connects VS Code to Google Cloud's Jupyter Notebooks
The Google Cloud Workbench Notebooks extension for VS Code allows developers to seamlessly connect their local IDE to managed Jupyter notebook environments on Google Cloud, enhancing ML workflow efficiency. This integration eliminates context switching, enabling smooth transitions from local experimentation to high-performance cloud computing.

