Topic starter
12/06/2026 12:12 am
TL;DR: AI model usage in cloud environments rose from 56% of organisations in 2024 to 84% in 2025, and OpenAI models dominate deployment patterns as cloud AI adoption scales, according to Orca Security. The governance problem is no longer AI enthusiasm, but visibility, control, and identity boundaries across expanding model estates.
Editorial analysis by NHI Mgmt Group, based on content published by Orca Security: “10 Most Popular AI Models of 2025”.
By the numbers:
- AI model usage in cloud environments jumped from 56% of organisations in 2024 to 84% in 2025.
- GPT-4o appeared in nearly 45% of cloud environments in 2025, at 44.72%.
Key questions
Q: How should IAM teams govern AI models in cloud environments?
A: Treat AI models as part of the cloud control plane, not as isolated tools.
Q: Why does AI adoption create governance risk even when individual tools seem inexpensive at first?
A: AI adoption creates governance risk because low-friction access encourages widespread use before controls are in place.
Q: When does AI model governance usually fail in practice?
A: It usually fails when documentation begins after deployment.
Practitioner guidance
- Inventory AI models as governed cloud assets Map every model, workspace, and managed AI service to an owner, business purpose, and data classification.
- Reassess service identities and workspace permissions Review the identities that create, invoke, or connect AI services, especially in Azure OpenAI and Azure Machine Learning environments.
- Classify retrieval paths as data-access channels Treat embedding, retrieval, and grounding pipelines as pathways to enterprise data rather than neutral model plumbing.
Bottom line: AI model adoption in cloud estates is now broad enough that governance has to cover inventory, ownership, and service trust, not just experimentation.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
This topic was modified 1 day ago by NHI Mgmt Group
12/06/2026 9:18 am
Model sprawl is now an identity governance problem, not just an AI adoption metric. Once AI models move from pilot use to broad cloud deployment, the primary risk shifts from experimentation to control boundary erosion. The article’s adoption data shows that visibility alone is no longer enough if the organisation cannot assign ownership, access scope, and review responsibility for each model and service path. Practitioners should treat model inventory as a governance prerequisite, not a reporting output.
A few things that frame the scale:
- 13% of organisations suffered breaches of their AI models, according to IBM's Cost of a Data Breach 2025 report.
A question worth separating out:
Q: Should organisations standardise on a small number of AI platforms?
A: Standardisation can simplify oversight, but only if the platform stack comes with clear ownership, service scoping, and data access rules. Without those controls, standardisation simply concentrates risk in fewer services and makes governance failures harder to spot.
👉 Read our full editorial: AI model adoption is reshaping cloud security governance in 2025
This post was modified 1 day ago by NHI Mgmt Group