Role-Based Access Control (RBAC) in Enterprise AI Search
A security architecture guide detailing how enterprise AI search engines enforce role-based access control, document permissions, and tenant isolation.
Role-Based Access Control (RBAC) in Enterprise AI Search
Security and data privacy are the primary hurdles preventing enterprises from deploying Large Language Models (LLMs) and AI search engines across their organizations.
If an AI search engine indexing Slack, Google Drive, Jira, and GitHub allows a junior employee to ask:
"What are the salary bands for senior engineering managers, and what was discussed in executive compensation meetings?"
...and the AI engine returns confidential HR documents, the system represents an unacceptable security risk.
In this architecture guide, we evaluate how Memora enforces Role-Based Access Control (RBAC), document permissions, and tenant isolation during Graph RAG retrieval.
Security Requirement: An enterprise AI search platform must never return search answers derived from documents or Slack channels that the querying user lacks explicit permission to access.
The 3 Levels of Enterprise Search Security
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β ENTERPRISE AI SEARCH SECURITY PIPELINE β
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β Level 1: Identity Sync (Okta / Azure AD OAuth Group Ingestion) β
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β Level 2: Graph Node ACL Metadata Tagging (Channel & File Access Control)β
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β Level 3: Real-Time Pre-Retrieval Permission Filtering (Zero Leakage) β
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Level 1: Identity Provider Synchronization
Memora integrates directly with enterprise identity providers (Okta, Azure AD, Ping Identity). User group memberships, department tags, and security clearances are synchronized in real-time.
Level 2: Graph Node ACL Metadata Tagging
When background ingestion workers index a Slack message, GitHub PR, Jira ticket, or Google Doc, they attach Access Control List (ACL) metadata to the knowledge graph node:
{
"node_id": "DOC-FINANCE-Q3",
"node_type": "GoogleDoc",
"title": "Executive Financial Projections",
"acl_permissions": {
"allowed_users": ["user_exec_12", "user_cfo_01"],
"allowed_groups": ["grp_finance_executives"],
"is_public_tenant": false
}
}
Level 3: Pre-Retrieval Permission Filtering
When a user submits a search query, Memora filters vector candidates and graph traversal edges before context is assembled into the LLM prompt window:
// Cypher Graph Traversal with Strict Pre-Retrieval ACL Filtering
MATCH (user:User {id: $querying_user_id})-[:MEMBER_OF]->(group:Group)
MATCH (q:Entity)-[r:DISCUSSED_IN|MODIFIED_BY]->(doc:Document)
WHERE doc.allowed_groups IN group.id OR doc.allowed_users = user.id
RETURN q, doc, r;
Guarantees Enterprise Security Teams Require
- Zero LLM Data Retention: Enterprise data payloads are never stored by third-party model providers or used for foundation model training.
- SOC2 Type II & ISO 27001 Compliance: Audited data encryption at rest (AES-256) and in transit (TLS 1.3).
- Tenant Isolation: Multi-tenant database partitioning ensures tenant data boundaries are physically or logically immutable.
Learn more by reviewing Memora Security Specifications.
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