Access Control in Atlan covers identity and access management (SSO, SCIM, users, groups, roles), permissions (personas, purposes, connection access, visibility), and audit logs. Configure who can sign in, what they can see, and how their activity is tracked.
Reference for all Labs Access control toggles in Atlan Settings, covering guest requests, member reporting, asset visibility, persona switcher, sample data download, Tableau embedding, and workflow admin access.
Manage the people in your Atlan workspace and what they can do: add users by email or identity provider, assign the role that sets their permissions, keep roles in sync, and offboard users when they leave.
Add or remove users and groups from a persona in Atlan to control who inherits its access policies and view.
Export administrative data into Excel — users, groups, personas, purposes, and policies for backup, audits, and offline access model analysis.
Establish robust AI governance frameworks that maintain compliance, mitigate risks, and drive trust through visibility, lifecycle management, and policy enforcement
Configure AI asset access in personas: control who can view, edit, or manage AI models, model versions, applications, and governance properties.
Migrate glossary terms, tags, and metadata from Alation to Atlan—configure asset mapping, image handling, and custom metadata preservation.
Automatically link glossary terms to matching assets — map terms by name patterns, asset types, and custom filters.
Bulk import glossary terms and assets from CSV files — configure term hierarchy, relationships, and business metadata.
Bulk import tag definitions into Atlan from a CSV file. Reference for workflow configuration, CSV format, tag properties, and asset-storage options.
Use the User Role Sync app to automatically assign Atlan roles based on group membership, with regex matching and role hierarchy controls.
Use the Asset-term link app to automatically create and manage links between glossary terms and matching assets based on names and filters.
Automatically downgrade users to the Guest role when they leave, using the offboarding app. Set up a workflow that revokes access based on group membership, with no manual edits per user.
Set up and configure BigQuery for data quality monitoring through Atlan.
Block users from querying or previewing data in Atlan at different levels: workspace-wide, by connection, by specific asset, or by tag.
Learn how to create and maintain a centralized business glossary in Atlan to standardize terminology and definitions across your organization.
Configure glossary access in personas: control who can create, update, and delete terms and categories, link terms to assets, and manage tags and custom metadata within a glossary.
Add or remove tags from a purpose in Atlan to change which assets fall within its scope and are covered by its policies.
Understand how Data Quality Studio uses compute resources, how costs are calculated, and practical ways to track and optimize spend for Snowflake, Databricks, and BigQuery.
Automate metadata enrichment at scale using AI-powered context agents that generate descriptions, READMEs, and SQL intelligence across your most important data assets.
Learn how to manage data contracts and agreements in Atlan to ensure data quality and compliance.
Copy a policy from one purpose to another in Atlan to reuse access configurations without rebuilding them from scratch.
Copy a policy from one persona to another in Atlan to reuse access configurations without rebuilding them from scratch.
Learn how to create an AI app in Atlan.
Learn how to create an AI model in Atlan.
Create or update metadata policies that target multiple assets at once, using advanced selection patterns such as every dashboard in a Looker folder, every project in Tableau, or every asset across selected connections. Uses the Metadata Policy Helper app.
Understand how credits are consumed by context agents in Context Agents Studio and how to track usage in the Reporting section.
Learn how to create and manage custom metadata attributes in Atlan to extend your data catalog with organization-specific information.
Expand existing custom metadata definitions to include new connections or glossaries added after initial creation. Reference for workflow configuration and scope-extension rules.
Complete reference for custom metadata property configuration, including all field types, settings, and asset targeting options.
Customize the catalog view a team gets in Atlan: set the default landing page, which asset types, sidebar tabs, and discovery filters appear, and what custom metadata is visible, per persona.
Configure data access in personas and purposes: control who can query and preview data, and apply column-level masking such as hashing, redaction, or showing only the last four characters.
Create and manage data products to organize and govern your data assets by domain.
Monitor and maintain data quality across your data sources with automated quality checks, alerts, and governance workflows
Set up and configure Databricks for data quality monitoring through Atlan.
Configure domain access in personas: control who can read, update, and manage data domains, subdomains, and products, including their custom metadata.
Link AWS SageMaker Unified Studio assets to Atlan Data Domains for unified governance.
Learn how to organize and manage domains in Atlan to structure your data assets in a logical and business-aligned way.
Step-by-step guide to triggering AI-powered metadata enrichment using context agents in Context Agents Studio.
Understand how context agents process knowledge files to extract business rules, create glossary terms, and generate skill files
Reference documentation for the GLOSSARY_DETAILS table containing glossary, category, and term details
Keep metadata current, enrich quality alerts with lineage context, propagate PII tags through lineage, and score assets for AI readiness using Atlan MCP.
Build an enterprise context layer for AI agents across multiple domains, governed for compliance, using Atlan Context Studio and the Atlan MCP server.
Reference for the User Role Import app in Atlan: workflow configuration fields, supported input methods (direct upload or object storage), and the required CSV format for bulk role assignment.
Build a golden dataset of unstructured business content in Atlan—SOPs, policies, and compliance rules—as governed, discoverable assets that context agents use for enrichment and skill generation
Understand knowledge folders and knowledge files as catalog asset types in Atlan.
Use Lakehouse to run advanced lineage analysis across systems and connectors.
Export complete lineage information from your metadata lakehouse for reuse across impact, dashboard, and root cause analysis.
Use Lakehouse to find all downstream dashboards impacted by upstream data changes.
Use Lakehouse to understand downstream impact before making changes to data assets.
Use Lakehouse to trace upstream dependencies and find the source of data issues.
Use Lakehouse to measure and improve tag coverage and propagation across systems.
Control who can administer a connection in Atlan by assigning or removing connection admins. Connection admins can edit, delete, and fully manage all assets under a connection.
Use Lakehouse to track metadata enrichment coverage across assets and systems.
Answers to common questions about Context Agents Studio—covering enrichment behavior, collections, agent support, processing time, and AI credit usage.
Use Lakehouse to export rich metadata for AI applications, data marketplaces, and syncing context back to source systems.
Configure metadata access in personas: control who can view, edit, or restrict metadata, tags, terms, and governance properties.
Configure metadata policies at scale using policy templates to automate permission assignment across assets, taxonomies, and custom metadata.
Control what users can see and do in Atlan once they have signed in. Scope access by team with personas, by data sensitivity with purposes, by connection, or hide assets entirely. Learn which control to use and how role, persona, and purpose combine.
Personas in Atlan scope what a team sees and can do by bundling users and groups with access policies and a custom catalog view.
Propagate tags or custom metadata from child assets to parent assets using configurable priority rules. Reference for workflow configuration, priority-rule syntax, and transformation settings.
Create a purpose in Atlan to scope access by asset tag. Pick one or more tags, attach policies, and protect every asset that carries those tags now and in the future.
Use the Designation-based group provisioning app to automatically place each new user into the right Atlan groups at first login, based on their designation.
Purposes in Atlan scope access by asset tag so every asset marked as PII, confidential, or any sensitivity label gets the same policies applied automatically.
Monitor governance metrics in the reporting center dashboard. Track query access for personas and purposes, view tag propagation across assets, and manage governance requests from a centralized view.
Request and manage changes to assets that you don't have direct edit access to.
Revoke a user's query and preview access on a specific governed asset in Atlan, and optionally raise a Jira or ServiceNow ticket to revoke that user's access at the source.
Configure BigQuery to enable data quality monitoring through Atlan.
Configure Databricks to enable data quality monitoring through Atlan.
Configure Snowflake to enable data quality monitoring through Atlan.
Create a persona in Atlan to scope what a team sees and can do: define access policies, add members, and set the UI experience for a team or function.
Set up and configure Snowflake for data quality monitoring through Atlan.
Automatically populate asset owners in Atlan using source-system metadata like Snowflake last_ddl_by and Tableau source_owner. Reference for workflow configuration and owner-mapping rules.
Learn how to implement data stewardship in Atlan through automated workflows, policies, and task management.
Use the reporting center to track asset enrichment, monitor metadata updates, and review governance metrics. Access dashboards for asset metrics, glossaries, governance, queries, automations, and usage and cost tracking.
Sync AWS Lake Formation tags to Atlan custom metadata to maintain governance alignment. Reference for workflow configuration, tag mapping, and input-file format specifications.
Learn how to use tags in Atlan to categorize and organize your data assets for improved discoverability and governance.
Resolve common issues with Confluence Integration Manager credentials and authentication.
Understand the collections available in Context Agents Studio—curated groups of data assets automatically surfaced from usage signals to help you prioritize metadata enrichment.
Learn about the AI-powered context agents available in Context Agents Studio—specialized agents that generate descriptions, READMEs, and SQL intelligence.
Upgrade your Snowflake data quality setup to the latest version
Let Atlan suggest data quality rules automatically based on your asset's metadata structure, and apply them in a few clicks.
Learn how to manage users and groups in Atlan to control access and organize your data team.
View and filter events received from connectors in Atlan to track lineage and observability data. Event logs are stored for 7 days.
Track all queries run in Atlan from the query editor, API, and sample data previews. Includes run status, user, connection, and timing. Query logs are retained for the lifetime of your Atlan instance.
View audit logs to track configuration changes made to your workflow connections.
Tags in Atlan classify data assets — use them to identify sensitive data (PII, confidential), group assets by domain, and drive granular access control through purposes.
Atlan MCP use cases across chat-based AI tools (Claude, Cursor, ChatGPT, Gemini), automation platforms (Python, n8n, LangChain), and end-to-end workflows for metadata enrichment, governance, data engineering, asset lifecycle, and catalog adoption.
Understand Atlan's Data Quality Studio and how it enables business and data teams to collaborate on defining, monitoring, and enforcing data quality expectations
Learn how to write custom instructions that help Context Agents Studio generate accurate, business-relevant metadata descriptions for your organization.