Why model selection is a governance decision

Not every task requires the same model. In an enterprise setting, selecting a model requires balancing quality, data sensitivity, regional requirements, risk, cost and approval obligations.

CODE S Analysis

Why model selection is a governance decision

Not every task requires the same model. In an enterprise setting, selecting a model requires balancing quality, data sensitivity, regional requirements, risk, cost and approval obligations.

8-minute readEnterprise AI governance

Classify data first

Public, internal, confidential and restricted data require different approved models and infrastructure options.

Policy before preference

Users should not have to make privacy decisions. The platform first filters out all impermissible options.

Quality, cost and speed

Within the permitted set of options, routing can optimise for task type, context size, quality, latency and cost.

Document the decision

The reasons for selecting or rejecting a model should be recorded in the audit trail.

From policy to infrastructure

CODE S implements governance requirements through identity controls, routing, policies, approvals and audit evidence.

Note: This article describes a technical operating model and does not constitute legal advice.

Apply policies from the start

Enterprise AI requires clear policies, traceable decisions and central control.

Your existing models, data and systems remain in place. CODE S adds a central control layer across them.

Manage models centrallyGovern AI agents according to defined policiesProtect access to data

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