AI Governance Studio

Govern every AI asset, across every system you run.

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Inventory Every AI Asset

AI applications, LLMs, MCP servers, and agents as first-class entities across AWS, Databricks, Snowflake, and beyond

Map to Major Frameworks

EU AI Act, NIST AI RMF, ISO/IEC 42001, and custom. Policies cascade across frameworks

Export Evidence on Demand

Machine-readable audit packs generated from the live inventory, for auditors and regulators

One cross-system AI registry, with lineage

AI applications, LLMs, MCP servers, and agents as governed assets, wherever they run, traced back to the data that feeds them.

Multi-Type Registry

AI Applications, LLMs, MCP Servers, and Agents, each with type, owner, risk, status, region, and PII flag

Cross-System

Govern and discover AI across AWS, Databricks, Snowflake, and beyond from one place, not one silo per system

End-to-End AI Lineage

Trace from data product to MCP tool to the agent that consumes it, in a single knowledge graph, so you can see what an agent can touch

Same Graph as Your Data

AI governance and data governance on one platform, through the same RBAC engine and metadata graph that already runs your estate

Multi-jurisdiction compliance frameworks

EU AI Act, NIST AI RMF, ISO 42001, plus regional baselines and custom frameworks.

Built-In Frameworks

Includes EU AI Act, NIST AI RMF, ISO/IEC 42001, Singapore Model AI Governance Framework, and others

Controls Apply in Scope

Enable a framework and its controls apply to every AI asset whose region, deployment, or risk class falls in scope

Readiness Score

Each framework tracks a live compliance score across the estate

Custom Frameworks

Build a custom framework with the controls and assessments your team needs for an internal regime

Governed intake and continuous policies

A structured intake for every new AI asset, and policies that run continuously across the estate.

Structured Intake

Every submission runs through different checks: owner assigned, risk classified, fairness evidence attached, DPIA referenced, transparency disclosure filed

Risk Council Workflow

Route submissions to the reviewers your policy dictates, with configurable approval chains and decisions logged for documentation

Cross-Framework Policies

Enable "human oversight required" and count it toward EU AI Act, NIST AI RMF, ISO 42001, and every framework that requires it

Continuous Enforcement + Breach Detection

Policies run continuously; when one is breached (missing DPIA, expired retention, undeclared PII), the system flags it and triggers remediation

Machine-readable audit packs

Export evidence for any framework on demand. JSON, machine-readable, generated from the live inventory.

Bundled Evidence

Compliance records, control coverage, workflow history, and remediation snapshots bundled into a downloadable pack

Any Framework, Any Scope

Export a pack scoped to one framework (ie - EU AI Act) or the whole data estate

Always Current

Generated from the live inventory, so it stays current as models, agents, and policies change

Auditor-Ready Format

JSON structured and machine readable for auditors and regulators

Built for modern data & AI practices

Designed for changing needs of data & AI teams

AI-Driven Automation

Improve productivity, enforce governance and reduce costs with AI driven automation

Unified Platform

One platform for all your teams for data discovery, observability and governance

Collaborate Around Data

Accelerate development of data assets with social workspaces and knowledge centers

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Managed Service for Production Data Teams

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FAQs

AI Governance Studio is a Collate feature that registers, governs, and audits every AI asset (applications, LLMs, MCP servers, and agents) across your whole data estate, on the same knowledge graph that powers your data catalog. It maps assets to regulations like the EU AI Act, exports machine-readable audit packs, enforces policies continuously, and traces AI lineage back to the data. It ships as a release preview with Collate 2.0.

AI Lineage traces each AI asset back to the data that feeds it: from the data product, through the MCP tool, to the agent that consumes it, all in a single graph. Because Collate already governs your data, the AI governance layer connects directly to the data governance layer, so you can directly show what an agent can touch.

A number of frameworks are included, such as EU AI Act, NIST AI RMF, ISO/IEC 42001, Singapore Model AI Governance Framework, and others. You can also build a custom framework for an internal regime. Enable a framework and its controls apply to every AI asset whose region, deployment, or risk class falls in scope.

Policies are enforcement rules, such as "human oversight required," "audit log retention 90 days," "PII access requires DPIA." A single policy can count toward multiple frameworks: enable "human oversight" once and it satisfies EU AI Act Article 14, NIST AI RMF MAP 3.5, ISO 42001 A.9.2, and every other framework that requires it. When a policy is breached, the system creates a remediation task automatically.

An audit pack is a JSON evidence bundle scoped to a framework (for example the EU AI Act) or the whole estate. It includes compliance records, control coverage, workflow history, and remediation snapshots, generated from the live inventory. Because it comes from the assets, it stays current as they change. These packs are machine-readable for regulators and auditors.

AI Governance Studio is a sibling to Collate's data governance surface (Glossary, Classifications, Policies, Domains, Lineage). It uses the same RBAC engine, the same activity feed, and the same knowledge graph, so teams who already steward data assets use the same patterns to steward AI assets.