We build governed data platforms on Databricks with Unity Catalog at the core — one set of access controls, column-level lineage and a full audit trail across every table, dashboard and AI agent. Plus real-time analytics on Databricks SQL. When a customer's risk team or a regulator asks where a number came from and who could see it, you have the answer, not a project to go find it.
Most organisations don't lack governance policies. They lack governance that's actually enforced at the point the data is used — and every gap between a warehouse, a lake, a BI tool and an AI model is a place where the policy and the reality drift apart.
One set of rules in the warehouse, another in the lake, a third in the BI tool, a fourth wherever the AI runs. Nobody can say, in one place, who can see what.
The question that ends meetings. Without lineage, tracing a figure on a dashboard back to its source is archaeology — days of work, and you're still not sure.
Who accessed the customer table last quarter? If the honest answer is "we'd have to reconstruct it," you're not ready for the audit that's coming.
Even well-governed data becomes ungoverned the moment an AI agent reads a copy of it. The controls have to reach the model, or they don't count.
One place to decide who can see and do what — down to the row and the column — across every table, view and dashboard. Fine-grained, attribute-based, and the same whether the reader is a person, a query or an agent.
Person · query · agent — one ruleAutomatic, column-level lineage from source to dashboard. Every field traces back to where it came from and forward to everything it feeds — so "where did this come from?" is a click, not a project.
Source → dashboard, captured automaticallyA complete, queryable record of who accessed what, when, and how. The trail an auditor asks for first — already there, not reconstructed under pressure.
Who · what · when — already queryableDatabricks SQL serving governed data to your BI and your analysts at warehouse speed — the same governed tables, queried live, with none of the controls left behind.
Governed tables, queried liveUnity Catalog sits above everything — the raw tables, the curated tables, the dashboards, the notebooks and the agents — and governs them from one place. Every consumer, human or machine, goes through the same set of controls. There is no side door where an AI model reads an ungoverned copy, because there is no ungoverned copy.
Column-level lineage is captured automatically as data moves through the platform. Pick any field on any dashboard and you can walk it backwards to the exact source columns it came from, and forwards to everything downstream that depends on it. When a source changes, you know instantly what breaks. When an auditor asks where a figure originated, you show them the graph.
Our AI Governance practice and this platform are two halves of one answer, and it's worth being precise about which is which — because a serious buyer will ask, and most vendors blur it. One is a way of working. The other is a technical control that makes the way of working real.
Unity Catalog at the centre, with the serving, security and integration tools around it. A sample across the layers:
The governed ETL we built feeding this supplier's live BI runs on exactly this control plane — access, lineage and audit under one catalog, data an analyst and an auditor can both trust. On the insurance side, intelligent document processing over ACORD forms and policy records, governed the same way. Governance that's operating in production, not a slide about governance.
The same team that designs your Lakehouse sets up its governance — so access, lineage and audit are designed in from the first table, not retrofitted by a separate team who didn't build it.
Because we build agents on AgentBricks too, we govern the whole path — the same catalog controls the analyst's query and the agent's context. Most vendors govern the tables and lose the model.
Our AI Governance practice brings the framework; this platform enforces it. You get the policy and the control from one partner who makes them fit together, instead of a consultancy and a tools vendor who don't talk.
ISO/IEC 27001:2022 certified, working to the access and audit discipline a European risk review actually tests — not governance theatre.
A customer's security review wants to know exactly who can see their data. We can't answer cleanly.
Our data access grew organically and now no one can tell me who has access to what.
An auditor asked for a year of access logs and we had to go build them.
We govern our warehouse well — and then our AI reads a copy nobody's watching.
Someone changed a source table and three dashboards broke before we noticed.
We need one place to prove data governance, not five consoles that half-agree.
Usually a CDO, Head of Data Governance or Data Platform, a CISO, a DPO, or a CTO whose customers run hard security reviews — in the US, Europe and APAC.
The Lakehouse this governs — the pipelines and the medallion layers the catalog sits over. Governance assumes there's something well-built to govern.
The agents this control plane reaches — production AI governed by the same catalog as your analysts, reading governed data, not a copy.
The method this platform enforces — the nine-phase lifecycle, the EU AI Act mapping, the evidence packs. Policy and control, together.
AI Governance is the method — a nine-phase lifecycle, risk tiering and evidence packs that decide whether an AI system is fit to ship. This is the mechanism — the Unity Catalog controls that technically enforce who can see what and prove where data came from. The method decides; the platform enforces. You need both, and we do both.
No. Unity Catalog can federate with an enterprise catalog you already run — Purview, Collibra, Atlan — so it governs your Databricks estate without forcing a rip-and-replace of your wider cataloguing. Where it makes sense to consolidate, we'll say so.
It reaches the AI. Because agents built on AgentBricks authenticate through the same catalog, they're subject to the same access controls and masking as a human user — and their access is in the same audit log. That's the whole point of doing data governance and AI in one platform.
Automatic. Unity Catalog captures column-level lineage as data flows through queries and pipelines — you don't hand-maintain a mapping. That's what makes "where did this come from?" a click instead of a project.
Yes — row filters, column masks and attribute-based policies, so the same table shows different things to different users. A support agent sees masked PII; a compliance officer sees it in full; an AI agent sees only what it's cleared for.
A complete, queryable access history — who accessed what, when, from where — plus the lineage graph showing how any figure was derived. The two things an audit asks for first, already there rather than reconstructed under deadline.
Databricks on AWS, Azure or GCP. It governs the data on the Lakehouse and integrates outward to your BI tools and enterprise catalog, so it fits into your estate rather than replacing it.
Start with a governance readiness review — we'll look at how access, lineage and audit work across your data and your AI right now, where the gaps are, and what a Unity Catalog control plane would close. The fastest way to know how you'd do in a customer's security review before they run it.