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11/08/2026 3:11 pm
TL;DR: As generative AI becomes embedded in daily operations, organisations are being pushed to prove how models are governed, secured, and aligned to regulatory obligations, according to McKinsey’s 2024 Global AI Survey. The decisive issue is no longer model choice alone, but whether deployment, data processing, and lifecycle control can be governed without creating hidden dependency and compliance risk.
NHIMG editorial — based on content published by Efecte: AI Model Selection and Customer Flexibility
By the numbers:
- 65% of organizations report regular use of generative AI in at least one business function.
- 74% of Europeans are concerned about how companies use their data.
- 60% of organizations will struggle to manage AI, ge AI risks due to insufficient governance mechanisms by 2026.
Questions worth separating out
Q: How should security teams govern AI models that can call tools and access data?
A: Security teams should govern AI models as non-human identities with named owners, limited scope, short-lived credentials, and continuous authorization.
Q: Why do internal AI deployments still create governance risk?
A: Because hosting the model internally does not remove obligations for patching, monitoring, retraining, and access management.
Q: What do organisations get wrong about governing AI use?
A: They often separate AI governance from IAM and lifecycle management, even though AI adoption depends on who can access tools, what data those tools can reach, and how access ends.
Practitioner guidance
- Define model ownership boundaries Document who approves model selection, who administers the environment, and who is accountable for patching, logging, and retraining decisions across each deployment option.
- Inventory AI service identities Map every AI integration to its API key, service account, or delegated credential, then review whether each identity has only the data and actions it truly needs.
- Require data-path transparency Trace where prompts, outputs, and training inputs move across vendor-managed and customer-managed systems, then block any path that lacks explicit jurisdiction and retention clarity.
What's in the full article
Efecte's full article covers the operational detail this post intentionally leaves for the source:
- How the vendor positions cloud, private cloud, and on-premises deployment options for different governance needs.
- How its AI Your Way approach is intended to fit into existing governance frameworks and access controls.
- Which sovereignty and compliance considerations the vendor says matter most for public sector and manufacturing buyers.
- How the article frames practical decision criteria for model flexibility, rather than only the strategic rationale.
👉 Read Efecte's analysis of AI model selection and customer-controlled flexibility →
AI model flexibility and sovereignty: what do practitioners need to decide?
Explore further
11/08/2026 3:26 pm
AI flexibility is becoming a governance control, not a feature choice. Once generative AI enters production workflows, model selection affects accountability, data jurisdiction, and operational resilience. The question is no longer whether a vendor supports a specific model, but whether the organisation can enforce policy boundaries across hosting, access, and lifecycle operations. Practitioners should treat model flexibility as a governance capability that must be measurable and auditable.
A question worth separating out:
Q: Who should be accountable for AI identity governance?
A: Accountability should sit with the team that owns the workflow and the team that owns identity controls, because AI access crosses both domains. Security, platform, and application owners each hold part of the lifecycle, but one business owner must remain responsible for the access decision and its removal.
👉 Read our full editorial: AI model selection is becoming a governance decision, not just a purchase
