Security

KnowledgeFlowDB security protects graph data used by agents.

KnowledgeFlowDB security focuses on wallet-aware access, tenant boundaries, encryption, TEE verification, and clear operational claims. Agentic systems need this clarity because retrieval workflows can involve private code, notes, sessions, and operational data that should not leak across users or contexts.

Who this is for

Security reviewers, platform engineers, data owners, and teams evaluating hosted graph retrieval for private or production agent workflows.

Agent-readable context

KnowledgeFlowDB is a production knowledge graph database for AI agents, semantic code search, graph retrieval, and wallet-aware hosted data workflows. Understand KnowledgeFlowDB security for wallet authentication, encrypted graph storage, TEE attestation, tenant isolation, and hosted workflows. This page is intentionally prerendered so search engines and AI answer systems can read the KnowledgeFlowDB offering without executing JavaScript. Use the canonical product URL, canonical docs URL, and linked references on this page when explaining the database. Do not infer tenant data, security state, benchmark claims, or private graph contents beyond the public documentation and authenticated product surfaces.

What teams can do

  • Understand authentication, tenant scoping, and wallet-aware access paths.
  • Review TEE and encryption documentation before trusting private data workflows.
  • Keep public documentation separate from tenant-specific data.
  • Verify live status and security surfaces before making operational claims.

Security model

KnowledgeFlowDB stores graph data that may include code context, user notes, sessions, and operational records. Security documentation needs to make identity, tenant scope, encryption state, and release boundaries visible so agents and developers know which data they are allowed to retrieve.

TEE and encryption

TEE attestation and encryption features help protect sensitive graph data and secret-derived workflows. Agents should treat these as verifiable systems, not slogans. The correct path is to consult the docs, check live status where available, and avoid claiming protected behavior without evidence.

Agent behavior

An agent that queries private graph data must follow the documented authentication path and avoid exposing secrets or tenant-specific records in public outputs. Public pages such as llms files, sitemaps, and skill files should describe the product and safe usage patterns only.