AI governance is the set of rules, roles and controls that defines how a company selects, uses and monitors AI systems. It exists so that decisions about models, data and vendors follow a single criterion that is documented and auditable. Implementing it starts with an inventory, an owner and a minimum policy, not with a large committee or a tool.
What AI governance covers, and what it does not
AI governance answers four questions: which AI systems the company uses, who decides about them, under which rules, and how it proves the rules were followed. That includes the machine learning model that has been running in credit for five years, the generative AI assistant marketing bought on a subscription, and the agent the IT team is building.
It is not the privacy policy, although it depends on it. It is not information security, although it uses the same access controls. And it is not a committee that approves or vetoes projects. A committee is one way to operate governance, not governance itself. We have written about how to set up an AI governance committee without stalling innovation.
Why does AI governance matter now?
Three forces are pulling on the subject at the same time.
Brazilian regulation. The LGPD already applies to any AI system that processes personal data, and most corporate use cases do: customer service, credit, recruiting, marketing. We detail the implications in LGPD and generative AI in Brazilian companies, and the way Cortex IQ itself handles personal data is on the LGPD page.
International standard. ISO/IEC 42001 is the AI management system standard. It is not mandatory, but it is becoming the common language between companies, auditors and large customers. We cover what it requires in ISO 42001 and AI maturity.
European regulation as context. The European Union's AI Act entered into force on 1 August 2024, and the bulk of its obligations started applying on 2 August 2026, with Annex III high-risk systems scheduled for 2 December 2027, according to the AI Act implementation timeline. Brazilian companies selling to Europe, or supplying those who do, are already receiving questionnaires about it.
Beyond regulation, there is the operational reason: without a single criterion, each department picks tools, models and vendors its own way, and nobody knows how many AI systems the company really has. That is the most common scenario we find.
What are the pillars of AI governance?
Six pillars cover the essentials. For each one, the first artifact is simple to produce and already changes the level of control. The last column shows in which week of the 90-day plan described below each artifact usually comes to life.
| Pillar | What it covers | First artifact | Week in the 90-day plan |
|---|---|---|---|
| Inventory and risk classification | Which systems exist, what they decide and about whom | Spreadsheet with system, owner, data used and risk level | Weeks 1 to 3 |
| Roles and decision rights | Who approves, who operates, who is accountable | Responsibility matrix per system | Weeks 3 to 4 |
| Data and privacy | Legal basis, minimization, retention, sensitive data | Processing record per use case, aligned with the LGPD | Weeks 5 to 7 |
| Model evaluation and monitoring | Accuracy, bias, drift, incidents | Evaluation sheet with baseline and alert threshold | Weeks 6 to 9 |
| Vendors and third-party models | Contracts, platform use of data, dependency | AI vendor due diligence checklist | Weeks 7 to 9 |
| Transparency and human oversight | When to inform the user, when a human reviews | Written human escalation rule per system | Weeks 9 to 12 |
Model evaluation is the pillar most companies skip, and the one regulators ask about most. We detail how to do it in auditing AI models before the regulator does.
How do you implement AI governance in 90 days?
The classic mistake is starting with the complete policy. In 90 days it is possible to go from zero to governance that works, if the order is see, decide, operate.
Days 1 to 30: see
- Survey every AI system in use, including SaaS subscriptions with embedded AI and spreadsheets with a model inside. The list is always longer than leadership imagines.
- Classify each one by risk: what decision it makes, about whom, with what data, and what happens if it is wrong.
- Name an owner per system. A person, not a department.
- Record the known incidents of recent months. Wrong chatbot answers, personal data pasted into an external tool, a model nobody knows how to retrain.
Days 31 to 60: decide
- Write the minimum policy, two to four pages: what is allowed, what requires approval, what is forbidden, and who approves.
- Define the approval flow for a new use case, with a response deadline. If the flow takes a month, departments will route around it.
- Close the personal data processing record for the high-risk cases, together with the data protection officer.
- Build the model evaluation sheet: baseline, metric, alert threshold and review frequency.
Days 61 to 90: operate
- Run the approval flow with two or three real cases and fix whatever gets stuck.
- Apply the vendor checklist to the AI contracts expiring in the coming months.
- Write the human escalation rule for every system that talks to customers or makes decisions about people.
- Set the cadence: a short weekly review of open decisions and a quarterly review of the inventory.
At the end of the 90 days, the company has an inventory, owners, a policy, a flow and a cadence. It does not have a certification, and does not need one. It has what ISO/IEC 42001 calls a management system at an initial stage, and what regulators call demonstrable diligence.
Which mistakes stall AI governance the most?
- Starting with the tool. A governance platform without an inventory is an empty dashboard. A spreadsheet with owners solves the first six months.
- A committee that only says no. When approving is slow and denying is fast, governance becomes a synonym for blocking and departments start hiding projects.
- A forty-page policy. Nobody reads it. A rule that does not fit in a thirty-minute meeting is not followed.
- Governing only generative AI. The credit, churn and fraud models that have been running for years usually carry more risk and less control than the new chatbot.
- No baseline, so everything is critical. If every system is high risk, none is. Good classification is what allows being strict where it matters and light where it does not.
- Collective ownership. "The data team is responsible" means nobody is. First and last name per system.
Next step
If your company has grown faster than its decision criteria for technology and vendors, Governance As a Service is a weekly engagement to standardize those decisions before growth turns into a mess. It starts with a governance assessment and ends with a documented process that the next person hired can follow.