Governance and access | Agentic Analytics

Governance for an analytics agent means deciding what data it can touch, who can ask what, and keeping a record of what happened. An agent that can query everything for everyone is a risk. Good governance keeps the data team in control while business users still get fast answers.

This is not an add-on. An agent that answers any question for any user, on any table, is not something a serious team can deploy. Limits are part of making it usable.

Controlling what the agent can see

The agent should only reach the data it is allowed to. You set this with access rules on tables and rows.

Table limits. The agent can query orders and customers, but not the HR tables.
Row limits. A regional user only sees their region’s rows.
Column limits. Sensitive columns like salary or raw PII stay out of reach.

These limits live in the context the data team controls, so the same rules apply no matter how a user phrases the question.

Who can ask what

Access is not only about the agent. It is about the people too. A user’s permissions should match what they could already see in the warehouse. The agent does not become a way around existing access control, it inherits it.

Auditability

You need to know what the agent did. An audit trail records the question asked, the SQL the agent ran, and the answer it gave. That record matters for two reasons.

Trust. When a number looks off, you can read the exact query and see why.
Compliance. You can show who asked what and what data was touched.

Without this trail, a wrong answer is a mystery. With it, you can trace any answer back to the query behind it.

Keeping the data team in control

The theme across all of this is control. The data team owns the context, the access rules, and the audit log. They decide what the agent can see and what correct means. Business users get self-serve answers, but inside boundaries the team set. An open-source agent like nao helps here, because the team can self-host and inspect exactly what runs. Once governance is in place, the last piece is keeping the agent reliable as the data changes.