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PRAX-IA Quórum
Data and AI governance. AI asks for permission too.
AI already queries your data. The question is with whose permission. PRAX-IA Quórum puts people and AI agents under one governance layer: every agent has a role and an owner, requests access to the table it needs like a person does, the data owner approves with an expiry date, and every query is logged.
- AI Ana’s agent requests accessTable sales_by_store · read only · 30 daysReason: reconcile the October close
- Q Quórum checks the requestRole: analysis agent · owner: Ana · sensitive data: noAna can see this table: the agent inherits, it never widens
- P The data owner approvesLaura, Finance · approved until November 25Sensitive data would require a second sign-off
- ✓ Access granted, and scopedThat table only, read only, expires on its ownNo service accounts that see everything
- ≡ Every query is logged10:42 · Ana’s agent · sales_by_store · 1,284 rowsWho asked, which agent, what data and who approved it
The problem
What gets stuck today.
- Agents with more access than any personTo make the pilot work, the agent got a service account that sees everything. Nobody knows what it queried, or for whom.
- Every team has its own numberSales, finance and operations report the same metric three different ways, and AI inherits the confusion.
- Data spread across cloudsPart lives in AWS, part in Azure, part in Google Cloud, each with its own permissions and no common rule.
How it works
Four steps, each one you can review.
- Catalog and meaningWhat each dataset means, who owns it and how sensitive it is, across your clouds.
- An identity for every agentEach AI agent gets a role, an owner and a scope, like an employee. No accounts that see everything.
- Access requested and approvedA person, or their agent, requests access to a table; the owner approves with an expiry and a reason.
- Every query loggedWho asked, which agent queried, what data it touched and who approved it.
What you get
Deliverables, not promises.
Everything lives in your cloud and in your name.
- Data catalog with owners, definitions and sensitivity levels
- Roles for people and for AI agents, with least-privilege access
- Access request flow with approval and expiry
- A log of every AI query: agent, person, data touched and approval
- Business glossary so AI uses the official figure, not an invented one
- Governance dashboard: active access, expiring access and pending requests
FAQ
What people ask.
How do you govern AI agents?
Like people: each agent has an identity, a role, an owner and a scope. It requests access to what it needs, someone approves it with an expiry, and every query it runs is logged.
Can each person’s agent see everything the company sees?
No. The agent inherits its person’s permissions and is scoped to the task. If it needs more, it requests it and the data owner decides.
Do we need to move data into one cloud?
No. We govern data where it already lives, in AWS, Azure or Google Cloud, and apply the rule in each place, building on what you already use, such as Microsoft Purview.
Is this only for large enterprises?
No. Mid-size companies adopting AI agents need it from the first agent, before access gets out of hand.
Also
More from PRAX-IA.
PRAX-IA Bridge
Move legacy data workflows to Fabric, Databricks, Snowflake, AWS or Google Cloud, reconciled to the last row.
See Bridge → Legacy BI to Power BIBI to Power BI
Legacy BI migration to Power BI: BusinessObjects, OBIEE and Qlik reports and semantic models rebuilt, with every figure checked against the original.
Learn more →PRAX-IA RelayAI agents
AI agents for mid-size companies: voice, chat and messaging agents with a test bench, cost per conversation and a person approving what matters. PRAX-IA Relay.
Learn more →Contact
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