Role-Based Access Control in Enterprise AI Search

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 walk through the common layers used to enforce Role-Based Access Control (RBAC), document permissions, and tenant isolation during Graph RAG retrieval, and note where Memora fits.

The 3 Levels of Enterprise Search Security

┌─────────────────────────────────────────────────────────────────────────┐
│               ENTERPRISE AI SEARCH SECURITY PIPELINE                    │
├─────────────────────────────────────────────────────────────────────────┤
│ Level 1: Identity & Source Permissions (Who can see what at the source) │
├─────────────────────────────────────────────────────────────────────────┤
│ Level 2: Graph Node ACL Metadata Tagging (Channel & File Access Control)│
├─────────────────────────────────────────────────────────────────────────┤
│ Level 3: Pre-Retrieval Permission Filtering                             │
└─────────────────────────────────────────────────────────────────────────┘

Level 1: Identity and Source Permissions

A permission-aware search system needs to know who the user is and what that user can already open in each source tool, whether that is a Slack channel, a Google Drive folder, a Jira project or a GitHub repository.

Level 2: Graph Node ACL Metadata Tagging

A common pattern is for ingestion workers to attach Access Control List (ACL) metadata to each indexed item, such as a Slack message, GitHub PR, Jira ticket, or Google Doc:

{
  "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

The safest design filters vector candidates and graph traversal edges before context is assembled into the LLM prompt window. Memora's answers only draw on content the asker can already access. An illustrative query:

// 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

1. No Training on Customer Content: Customer content is never used to train AI models, and Memora's language-model requests go to an OpenAI Enterprise endpoint with zero data retention.
2. Encryption & Compliance Documentation: Data is encrypted at rest (AES-256) and in transit (TLS 1.3). Security documentation, current compliance status and a DPA are shared on request.
3. Tenant Isolation: Data is hosted on AWS in the USA or EU, and Enterprise plans run on dedicated single-tenant infrastructure.

Learn more by reviewing Memora Security Specifications.

Related Articles & Resources

• Definitive Guide to AI Knowledge Management Platforms
• Best AI Knowledge Management Platforms
• Memora Security Policy