AI Governance
No one in the organization can explain which model made which decision, or with what data. We define the policies, roles, and clear controls over how artificial intelligence is used within your company — who decides, who audits, which data can be touched and which can't.
Governance that can be audited, not just declared.
AI usage policies
Who can use which model, for what business case, and with what data — in writing, not as a tacit agreement between areas.
Roles and owners
Who approves an AI initiative before it starts, and who audits the outcome once it's in production.
Data controls
What information a model can touch and what stays out of scope, with traceability for every automated decision.
Without a governance framework, every AI initiative is left to the judgment of whoever implemented it. When something goes wrong — sensitive data exposed, an automated decision with no traceability — there's no one to turn to and no way to audit what happened.
This framework runs transversally beneath the CIRA method's four phases, so every AI decision — from diagnosis to adoption — stays documented and auditable.
Preguntas frecuentes
We start from zero: we define policies, roles, and controls from week one, without stopping initiatives already underway.
We audit what already exists and integrate it into the same governance framework, instead of asking each area to start from scratch.
We document the framework so it withstands an external audit — roles, policies, and decision records, not just an internal presentation.
The goal isn't slower approval — it's approval with judgment. Most initiatives move faster once there are clear rules for what's in and what's out.
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