We had a sprawl problem: teams picked different models for similar tasks, and that created inconsistent handling of sensitive data. I started by creating a curated list of approved models for different use cases (PII processing, content generation, analytics). For each approved model I documented constraints: allowed data types, masking requirements, and whether outputs could be cached.
Then I added a gating policy: workflows that call unapproved models are flagged and require an exception ticket. For uniform data handling I enforced a pre-call masking step and a post-call retention policy. The result was fewer surprises and clearer audit trails.
How do you manage model selection at scale and prevent accidental use of noncompliant models?
I maintain an allowlist of models per use case and block others. Templates only expose approved models. For exceptions, use a ticket and approval flow. https://latenode.com
We published a simple model matrix: rows are models, columns are allowed use cases and data sensitivity levels. Every workflow UI shows the matrix entry for the selected model. If a user picks a risky combination, the UI warns and requires a justification. That education plus friction reduced risky choices quickly.
Also enforce automated checks: scan workflows for model IDs and run a preflight that rejects runs using disallowed models unless an approver signs off. Combine that with logging of model id, version, and the data handling steps taken before/after the call.
At scale, centralization beats micromanagement. We created a central registry of vetted model profiles managed by a small governance team. Profiles included allowed input categories, redaction rules, and retention limits. Teams could request new profiles via a fast-track review. To avoid slowing teams, the registry had clear SLAs: a 48-hour review window for low-risk requests and a 7-day window for higher-risk ones. That balance kept velocity while ensuring consistent handling and easy audits.
Model governance requires three pillars: policy, automation, and visibility. Define policies for model usage, automate enforcement in the pipeline (allowlists, preflight checks, masking), and provide dashboards that show which models are used where. Periodically review model performance and compliance posture. Ensure all model calls are logged with identifiers that link back to model profiles—this is essential for audits and post-incident reviews.
use a central allowlist + preflight checks. document exceptions.
enforce allowlist + preflight
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