Legacy Repositories, Wikis, Cloud Storage
AI Models & Assistants / Knowledge library
Glean
permission-aware enterprise search, assistant and agents
Why learn Glean?
Useful for understanding enterprise AI systems that combine search, assistant behavior, agents and permission-aware company knowledge.
Where it came from
Glean says it grew from workplace search into an enterprise AI platform grounded in company context, permissions and governance.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Summarize the official positioning for Glean in one paragraph.
- Identify the buyer, developer, or user workflow Glean is built for.
- Compare Glean with a neighboring catalog topic and name the boundary.
- List two implementation risks or adoption questions for Glean.
- Design a five-question evaluation that would test whether someone understands Glean.
Documentation and sources
- Glean about page (www.glean.com)
Catalog research notes
- Research snapshot: September 20, 2026. This entry summarizes the linked public documentation and should be refreshed before making version-specific claims.
Connect the ideas
Built on
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Real World Example
Unified R&D Knowledge Synthesis
Global pharmaceutical firms managing siloed research data across disparate cloud storage and internal wikis.
The business problem
Researchers lose significant time manually cross-referencing clinical trial results stored in legacy document repositories with current project status updates in modern collaboration tools, leading to redundant testing.
How this topic solves it
Glean provides a permission-aware enterprise search and assistant layer that indexes these disparate data sources, allowing researchers to query across boundaries while strictly enforcing existing access control lists (ACLs) to ensure sensitive clinical data remains restricted to authorized personnel.
- Connect Glean to existing document repositories and collaboration platforms via native enterprise connectors.
- Configure permission synchronization to ensure Glean respects the source system's original access policies.
- Define specific knowledge domains within the assistant to prioritize technical research documentation over general administrative content.
- Deploy the conversational assistant to allow researchers to query cross-platform project status and historical data in natural language.
This diagram illustrates how Glean acts as an intermediary between secure data sources and the end user, maintaining data integrity through permission mapping.
Permission-aware ingestion engine
Generative AI interface
Researcher
- Data Sources → Syncs content and ACLs → Glean Indexer
- Glean Indexer → Provides grounded context → Glean Assistant
- End User → Submits natural language query → Glean Assistant
- Glean Assistant → Returns filtered, authorized results → End User
Expected business outcome
Researchers achieve faster synthesis of historical trial data and current project status, reducing redundant work through improved visibility into existing organizational knowledge.
Limitations and checks
The system relies on the accuracy of source-system permissions; if legacy repositories have misconfigured or overly permissive access rights, Glean will reflect those same security gaps, requiring periodic audits of the underlying data source permissions.