---
title: "Active Data Governance: Built on the Context Layer for AI | Atlan"
url: "https://atlan.com/active-data-governance/"
description: "Data governance platforms gave compliance teams the rules. Atlan is the context layer for AI — giving every agent the same policies, classifications, and access context, automatically, before they act."
---

> Atlan is hosting Context Conference, bringing together the leaders and builders at the frontier of giving AI the context it needs to understand their business. It runs online on October 28, 2026, from 11:00 AM to 2:00 PM ET. Atlan co-founder Prukalpa Sankar opens and closes the day. Leaders from AstraZeneca, BNY and Verizon share why they invest in context and what they get from it. Registrants get early access to The AI Context Gap, a new study from MIT Technology Review Insights. Register: https://atlan.com/context-conference/

**Data governance platforms were the context layer for compliance. Atlan is the context layer for AI.** Before an AI agent acts on your data, it needs to know the rules: what's classified as PII, what policies apply, who can access what, what's certified and what isn't. Atlan surfaces that governance context automatically: propagated along lineage, enforced at access, validated before production.

[Book a Demo](https://atlan.com/forms/talk-to-sales-contact/) · [See How It Works](https://atlan.com/context-layer-demo/)

## The rules existed for humans. AI needs to know them too.

Data governance platforms built the policy layer for compliance teams: tagging PII, classifying assets, enforcing access controls, tracking what data exists and who can touch it. Written for humans who could read a policy doc.

A policy doc doesn't tell an agent whether the specific column it's querying right now is PII, under a data residency restriction, or certified for use. Without governance context embedded, agents operate blindly: they don't know a column is restricted or that access wasn't supposed to be granted, and they produce answers with data they shouldn't have used, violating rules they never knew existed.

Atlan is the [context layer for AI](https://atlan.com/context-layer/): the infrastructure that makes every AI agent in your stack accurate, trustworthy, and production-ready. The context layer is broader than governance (business knowledge, semantic definitions, lineage, quality signals, and the institutional expertise encoded in every SQL query and BI dashboard), but governance context is foundational to all of it.

Governance classifications (PII tags, security labels, compliance policies, data quality scores) propagate along [Data Lineage](https://atlan.com/data-lineage/) automatically to every downstream asset and every AI agent. Access is enforced at the [Data Marketplace](https://atlan.com/data-marketplace/) before agents or humans reach restricted data. [Context Engineering Studio](https://atlan.com/context-engineering-studio/) validates that agents respect governance before any context ships to production.

## From data governance to the context layer for AI

Before an agent acts, it needs to know what's classified, what it can access, and whether the context it's running on respects the policies your team has set.

- **Classification context propagates automatically.** Data Lineage carries governance context downstream. Tag a column as PII once and every downstream asset, pipeline, and AI agent inherits that classification. Security labels, compliance flags, and data quality scores travel with the data. [Data Lineage](https://atlan.com/data-lineage/)
- **Access context enforced at the source.** The Data Marketplace enforces access policies at the point of request. Zero-touch provisioning means the right agents and humans get the right data, and restricted data stays restricted. No manual queues. No policy exceptions. [Data Marketplace](https://atlan.com/data-marketplace/)
- **Policy context validated before production.** Context Engineering Studio test suites surface when an agent is using data it shouldn't, producing answers that violate policy, or missing classification context it needs. [Context Engineering Studio](https://atlan.com/context-engineering-studio/)

## Leading AI teams use Atlan to connect context

Trusted by $10T in enterprise value. Customer videos: Sridher Arumugham (DigiKey: Context Readiness), Kiran Panja (CME Group: Context at Speed), Andrew Reiskind (Mastercard: Context by Design), Mauro Flores (Virgin Media O2: Context for All).

## Propagate: tag once, every agent knows the rules

PII classifications, security labels, and compliance flags propagate along lineage to every downstream asset and every AI agent automatically, so governance context travels with your data, not behind it.

A living graph that compounds governance signals:

- **Quality compounds along lineage.** When a quality check fails upstream, lineage shows every downstream dashboard, pipeline, and AI agent affected. Root cause analysis goes from days to minutes.
- **Governance propagates along lineage.** Tag a column as PII once; lineage propagates that classification to every downstream asset and syncs bi-directionally with Snowflake and Databricks.
- **Impact analysis travels along lineage.** Before a data engineer ships a change, Atlan shows the full blast radius inside the GitHub or GitLab pull request: downstream dashboards, pipelines, AI agents, data products.
- **AI agents read the full context chain.** Through Atlan's MCP Server, AI agents query the lineage graph before they use data. An agent checking a column gets back provenance, quality score, governance policy, and ownership in one call.

"With Google DataPlex, lineage only showed part of the story. Our business operates across many systems and we needed complete, enterprise-wide lineage. Atlan's platform was more intuitive, delivered on complex end-to-end lineage, and had a strong library of connectors. We also used OpenLineage for Spark jobs to tie operational lineage to our data platform." — Zenul Pomal, Core Data Platform & Enterprise Architecture, CME Group

CME Group: **18M+** assets cataloged; **1,300+** glossary terms connected; **100+** active users. [Watch the case study](https://atlan.com/regovern-watch-center/cme-group-evaluation/)

## Enforce: access context, enforced automatically

Zero-touch provisioning means access policies run at the point of request: the right agents and humans get the right data, restricted data stays restricted, and no manual approval queue slows governance down.

The policy layer that never sleeps. The average access request takes two weeks, not because the data is sensitive but because nobody knows the policy. Atlan attaches policies directly to assets and enforces access automatically.

- **Policies attached to data assets.** Classification-driven, domain-aware, enforced at the point of discovery. When an asset changes, the policy changes with it.
- **Zero-touch provisioning with Immuta.** When a request is approved, Immuta enforces access in Snowflake or Databricks: masking, column-level restrictions, attribute-based access control. No tickets. No waiting.
- **Jira and ServiceNow for enterprise IT workflows.** Approved requests auto-generate issues with full context (asset, classification, approver, requester). Status syncs back in real time.
- **Built-in workflows.** Atlan's no-code workflow builder handles access management end to end: approval chains, auto-approval rules for low-sensitivity data, and a centralized request inbox.

"The UI was so intuitive that even first-time users could search, navigate and find what they needed. Within the first year after that we cataloged over 18 million assets, defined more than 1,300 glossary terms, and we are tackling new use cases every quarter." — Kiran Panja, MD, Cloud & Data Engineering, CME Group

## Validate: know your agents respect governance before they ship

Context Engineering Studio generates test suites from your existing dashboards and queries, surfacing when an agent is using data it shouldn't, missing classification context, or producing outputs that violate policy. Governance validated before production, not discovered after.

Built to solve the three biggest context challenges (AI context gap):

| Wall | Problem | Solved by |
|---|---|---|
| 1 · Cold Start | Context is scattered across every tool. You have a thousand AI use cases but don't know what data you have, what it means, or how to make it machine-readable. "You can create a cortex analyst in five minutes but your data has to be just right for it to work..." — Leading UK Retail Group | Context Bootstrapping: Context Engineering Studio reads your existing data graph to auto-generate a semantic layer you can build on. |
| 2 · Testing Hell | You can't test context manually at scale. Every persona asks different questions; a CEO needs trend lines, an analyst needs precision. "We've tested our customer service analytics chatbot for a month and a half and no one feels like they're at the end of testing. You can't test an infinite number of questions." — Global Lifestyle Brand | Context Engineering: auto-generate an eval suite from your existing dashboards and run every question against simulated personas. |
| 3 · Scaling Trap | More agents multiplies the inconsistency. When each agent holds its own context, updates diverge and answers conflict. "We had some early success, and now as we add more semantic models and data sources, experience has started to degrade..." — Leading CRM SaaS Company | Context Deployment & Observability: one shared context repo means every agent reads from and writes to the same, self-improving context. |

The page illustrates one shared context repo serving Cortex Analyst, Databricks Genie, Hex, Claude (MCP), ChatGPT (A2A), and Google Agentspace: same question, same answer, every agent.

"Atlan captures Workday's shared language to be leveraged by AI via its MCP server. As part of Atlan's AI labs, we're co-building the semantic layer that AI needs." — Joe DosSantos, VP Enterprise Data & Analytics, Workday

## Governance integrations: Cyera, Immuta, BigID

Your security and compliance stack already discovers sensitive data, classifies it, and controls access. Atlan turns those signals into shared context, so every AI agent and analyst operates within the rules your security and compliance teams set.

- **Cyera (DSPM).** Cyera discovers and classifies sensitive data across your cloud estate: PII, PHI, financial records, credentials. Atlan's native Cyera integration crawls those classification signals into the context layer; AI agents querying through Atlan's MCP Server receive Cyera's classifications before they act.
- **Immuta (access governance).** Immuta governs who can query what, under what policy conditions. The integration associates Immuta access request links directly with data assets in Atlan, so users and agents requesting access start the correct governed workflow without leaving discovery context.
- **BigID (data intelligence).** BigID maps privacy, compliance, and security risk across your data estate, labeling what's regulated, sensitive, or needs governance attention. BigID's policy intelligence enriches the assets Atlan surfaces to AI agents and analysts.

## Industry recognition

- **Forrester Wave™ Leader, Data Governance Solutions, Q3 2025:** "Its knowledge graph and AI-powered automation support clear data ownership, surfacing policy-relevant context and automating governance workflows centered on a metadata lakehouse." [Report](https://atlan.com/know/forrester-wave-data-governance-2025/)
- **Leader, 2026 Gartner® Magic Quadrant™ for Data & Analytics Governance Platforms:** "Leveraging the metadata lakehouse, the Atlan App Framework provides a structured toolkit and a marketplace to help developers, customers, and partners build, deploy, and share a wide range of applications, including custom connectors, data quality apps, and governance agents." [Report](https://atlan.com/gartner-magic-quadrant-data-governance-2026/)
- **Leader, 2025 Gartner® Magic Quadrant™ for Metadata Management Solutions:** "Atlan's Metadata Lakehouse forms the core foundation, built on an open and highly performant architecture. It is designed to be Iceberg native and includes a knowledge graph and event stream engine." [Report](https://atlan.com/gartner-magic-quadrant-metadata-management-solutions-2025/)

## Explore the platform

- [Data Lineage](https://atlan.com/data-lineage/): governance context that propagates automatically to every agent.
- [Data Marketplace](https://atlan.com/data-marketplace/): access policies enforced at the point of request.
- [Context Engineering Studio](https://atlan.com/context-engineering-studio/): validate governance context before production.
- [Context Agents](https://atlan.com/context-agents/): classification and quality context at scale.

## FAQ: data governance for AI agents

**What is the context layer for AI, and how does it relate to data governance?**
The context layer for AI is the governance context AI agents need before they act: what data is classified as PII, what policies apply, what they can and can't access, what security rules govern each asset. Data governance platforms built those rules for compliance teams. Atlan surfaces that context automatically to AI agents: classifications propagate along lineage, access is enforced at the Data Marketplace, and Context Engineering Studio validates that agents respect governance before they reach production.

**How does Atlan give AI agents governance context?**
Through Data Lineage, governance context travels with your data: tag a column as PII once and every downstream asset and AI agent that queries it inherits that classification. Through the Data Marketplace, access policies enforce at the point of request. Through Context Engineering Studio, governance context is validated before any agent ships to production. Agents don't need to know the rules separately; they're embedded in the context layer.

**How does PII classification reach AI agents automatically?**
Atlan propagates PII classifications along the lineage graph to every asset, pipeline, and AI agent in the dependency chain, and syncs bi-directionally with Snowflake and Databricks. AI agents querying those assets through Atlan's MCP Server receive the classification before they act on the data.

**What does data access control look like for AI agents?**
The Data Marketplace enforces access policies at the point of request. AI agents, like human users, only reach data they're authorized to access. Zero-touch provisioning runs the policies automatically, without a manual approval queue. Restricted data stays restricted regardless of who or what is asking.

**Does Atlan work with the governance and security tools we already have?**
Yes. Atlan connects with the specialist tools that already govern your data rather than replacing them. The Cyera integration pulls DSPM classification signals (PII, PHI, financial data, credentials) into the context layer. The Immuta integration surfaces access request workflows at the point of discovery. The BigID partnership feeds privacy and compliance intelligence into the assets Atlan manages. Your governance stack sets the rules; Atlan makes sure every AI agent knows them.

**Can Atlan replace our existing data governance platform?**
Yes. Atlan replaces your data governance platform with the context layer for AI, automating the classification, lineage, access governance, and policy propagation that manual governance tools need significant human effort to maintain. Atlan does not replace your DSPM or data access tools: Cyera and BigID discover and classify sensitive data and Atlan surfaces their classifications as governance context; Immuta governs fine-grained access and Atlan surfaces its request workflows at discovery, keeping enforcement where it belongs.

Classifications propagate along lineage. Access is enforced automatically. Context is validated before production. [Book a Demo](https://atlan.com/forms/talk-to-sales-contact/) (30-min call).