Authenticated access
Application and API access is tied to authenticated users, active organization context, public integration configuration, and provider-specific connection state.
Security and access control
Secure AI deployments
LLMLab is designed for organizations that need authenticated access, encrypted storage, structured permissions, scoped actions, and clear control over how AI workflows touch data and connected services.
A customer-facing support agent is one exposed surface. The same controls apply to internal workflows, knowledge bases, codebases, model paths, secrets, integrations, and hosted AI interfaces.
Security principles
Security starts with explicit system boundaries.
LLMLab workflows may read source-derived context, inspect workflow state, call configured APIs, store reusable answers, or expose hosted interfaces. Those capabilities need boundaries before they become production surfaces.
Permission-aware resources
Workflows, codebases, knowledge bases, models, nodes, secrets, and admin surfaces are designed to sit behind explicit access checks.
Protected credentials and payloads
Webhook URLs, API keys, OAuth state, stored text chunks, and logs can move through encrypted payload models rather than plain application values.
Identity and access
Organizations, roles, permissions, and controlled admin surfaces.
LLMLab supports organizations with memberships, roles, join policies, approval flows, and permission lists. That matters when workflows can touch customer-facing surfaces, source context, model paths, and configured API actions.
01
Organization-scoped access
Users operate inside organizations, with organization membership tracked separately from the base user account.
Organizations
Memberships
Active org
02
Role-based control
Roles can define who can administer, manage, run, approve, or view the resources behind a workflow, assistant, support agent, or hosted interface.
Owners
Admins
Custom roles
03
Workspace permission rules
Knowledge bases, codebases, workflows, nodes, and model surfaces can carry workspace-level access rules instead of relying on implicit trust.
View
Run
Manage
04
Admin-gated membership and resource management
Membership approvals, role changes, source management, secrets, and other sensitive operations are protected by admin-level requirements.
Approval flows
Admin access
Controlled changes
Encryption
Encrypted payload storage with managed and client-controlled key paths.
LLMLab supports Google Cloud KMS-backed encrypted payloads and a private-DEK path for customer-controlled decryption. The private-DEK path keeps the data encryption key client-owned, so LLMLab cannot decrypt protected private records without that key.
Google Cloud KMS-backed encryption
Connected-service payloads, webhook secrets, API keys, OAuth state, and knowledge base text chunks can be modeled as encrypted records with managed key custody.
Google Cloud KMS
Encrypted JSON
Managed custody
Private DEK encryption
For higher-sensitivity paths, organizations can use a client-held data encryption key for secrets, stored text chunks, and logs.
Client-held DEKs
AES-GCM
No server-side DEK storage
Recovery without plaintext custody
Recovery phrases can wrap private DEKs without turning LLMLab into a plaintext key escrow service.
Recovery phrases
HKDF-SHA256
Customer control
Encryption metadata travels with the data
Records carry encryption version, algorithm, key reference, nonce, and recovery metadata where applicable for safer rotation and migration.
Versioning
Rotation-ready
Metadata
Workflow and resource boundaries
Workflow controls apply beyond the login screen.
LLMLab scopes the resources an AI system depends on: knowledge sources, codebases, model paths, API actions, hosted interfaces, and runtime review surfaces.
Knowledge and codebase access
Sources, codebases, and knowledge bases are organization resources with permission-aware retrieval and management paths.
Configured API actions
HTTP actions use explicit destinations, request schemas, runtime mappings, and secrets instead of open-ended automation.
Model escalation boundaries
Model access, routing, validation, and escalation live inside the workflow and organization model rather than a hidden side channel.
Operational trust
Visibility matters when AI workflows run in production.
Teams improve AI systems by inspecting workflow runs, retrieval context, output, errors, action behavior, and usage signals after real interactions.
Inspectable workflow runs
Workflow execution is modeled node by node, making behavior easier to review than a single opaque prompt.
Structured action outputs
Configured actions can capture generated request bodies, status, errors, and runtime context for review.
Org-scoped knowledge retrieval
Knowledge search stays tied to selected sources and organization context so support answers use the intended material.
Optional anonymization
In addition to encryption and organization controls, LLMLab can apply data anonymization to remove identifying details from stored content.
Security direction
More controls as AI systems take on more work.
LLMLab's security direction includes richer audit surfaces, stronger action approvals, policy previews, and deeper customer-controlled encryption workflows.
Make sensitive operations reviewable.
Show who changed roles, ran workflows, touched sources, rotated secrets, or approved action paths.
See access changes before applying them.
Admins should be able to understand who gains or loses access before policy changes take effect.
Gate high-risk workflow actions.
Approvals, rate limits, and policy checks become more important as agents touch more operational systems.
Expand customer-owned encryption boundaries.
Private-DEK flows can grow into richer customer-managed key, rotation, and recovery models.
Build with boundaries
Access control and encryption are part of the system.
Use LLMLab to connect context, workflows, configured API actions, permissions, answer memory, model routing, hosted interfaces, and encryption in one managed platform.