Architecture — In-Tenant, Azure-Native, Governed by Design

Architectural PrinciplesCore Building BlocksHow to Read This DiagramWhy This Matters (Architect View)Summary: Where Failure Is Allowed

Architectural Principles

AI Fabrix runs entirely inside your Azure tenant, with no shared SaaS control plane and no external data boundary crossing.
Identity, policy, lifecycle, and audit are enforced in the control plane, while the data plane delivers structured, AI-ready pipelines for governed workflows and agents.
Integrations rely on open standards such as OpenAPI and MCP, ensuring transparency, portability, and no proprietary SDK lock-in.

Reference Architecture Diagram

Governed Request Lifecycle

Identity & Context

Authentication + workspace + role

Controlled Execution

Orchestration + MCP/OpenAPI + Dataplane filtering

Governance & Audit

Miso enforcement + audit + governed response

What Architects Validate

Data boundaries keep all data in-tenant, with egress allowed only by explicit policy.
Identity and authorization are enforced per user at the dataplane boundary, without shared service accounts.
Audit and governance establish deterministic audit trails and policy packs for RBAC, ABAC, quotas, and compliance.
OpenAPI and MCP contracts ensure integrations remain portable and inspectable.

Dataplane — CIP + Retrieval

CIP executes integrations inside the tenant
Metadata is normalized with lineage preserved
Retrieval enforces permissions

Orchestration — Microsoft Copilot, Flowise

Builds agents and workflows
Builds on governed Dataplane outputs
Versioned deployment through platform governance

Enterprise UX — Microsoft Copilot, OpenWebUI

Secure chat and collaboration
Workspace controls, RBAC, audit logs
Safe human-in-the-loop interaction

Core Building Blocks

Control Plane — MisoCIP
Controls who can do what, where, and under which policies:

How to Read This Diagram

Identity Is Established First

Authentication happens via Entra ID
Identity claims travel with the request
No anonymous or system-level access paths exist

Policy Is Evaluated Centrally

Miso evaluates RBAC, ABAC, environment, and egress rules
Decisions are deterministic and auditable
No policy logic is embedded in applications or workflows

CIP Supplies Data, Not Systems

CIP executes inside the tenant
Integrations do not bypass identity
No service accounts are used as a default pattern

Retrieval Is Permission-Aware

Filtering happens using metadata + identity context
AI never sees data it should not see
Lineage and scope are preserved

Response Is Governed by Design

The response reflects what the user is allowed to know
Audit records already exist — no reconstruction required
Same flow applies to humans, APIs, and AI agents

Why This Matters (Architect View)

This sequence shows why AI Fabrix scales safely:‍

Governance is structural, not procedural
Identity is never dropped
Data access is contextual, not static
AI does not introduce exception paths

The difference between traditional enterprise AI stacks and AI Fabrix is not capability, but architecture—traditional stacks allow failure modes by design, while AI Fabrix removes them structurally to enable AI at enterprise scale.

1. Identity Loss

Traditional AI Stacks

AI Fabrix

Traditional AI Stacks User identity is dropped early in the request flow

AI Fabrix Identity is preserved end-to-end

Traditional AI Stacks Service accounts and API keys represent many users

AI Fabrix Every request carries user context

Traditional AI Stacks AI operates as a privileged system actor

AI Fabrix AI acts strictly on behalf of an authenticated identity

What breaks traditionally

Over-exposure of data
Impossible audits
“Who accessed this?” cannot be answered
‍

Why Fabrix holds

Identity is the primary execution context
No default system-level access exists

2. Permission Leakage

Traditional AI Stacks

AI Fabrix

Traditional AI Stacks Permissions enforced in application code

AI Fabrix Permissions enforced in the data plane

Traditional AI Stacks Filtering logic duplicated per app

AI Fabrix One structural enforcement model

Traditional AI Stacks Edge cases create data leaks

AI Fabrix No exception paths exist

What breaks traditionally

Security bugs scale with integrations
AI answers expose sensitive data

Why Fabrix holds

Data is supplied already filtered
AI never sees unauthorized content

3. Governance Drift

Traditional AI Stacks

AI Fabrix

Traditional AI Stacks Governance configured manually per system

AI Fabrix Governance embedded by design

Traditional AI Stacks Policies differ across tools

AI Fabrix One policy model everywhere

Traditional AI Stacks Compliance requires negotiation

AI Fabrix Compliance is deterministic

What breaks traditionally

AI pilots stall at security review
Production differs from approved design

Why Fabrix holds

Governance is not optional or repeatable — it is structural

4. Audit Reconstruction

Traditional AI Stacks

AI Fabrix

Traditional AI Stacks Audit trails are incomplete

AI Fabrix Auditability is automatic

Traditional AI Stacks Data lineage must be reconstructed

AI Fabrix Lineage exists by default

Traditional AI Stacks Compliance reviews are forensic

AI Fabrix Compliance is explainable

What breaks traditionally

Audits become expensive investigations
Trust in AI outputs erodes

Why Fabrix holds

Every interaction is already logged, scoped, and traceable

5. Service Account Sprawl

Traditional AI Stacks

AI Fabrix

Traditional AI Stacks Integrations rely on service accounts

AI Fabrix No default service accounts

Traditional AI Stacks Privileges are static and over-scoped

AI Fabrix Access is contextual and dynamic

Traditional AI Stacks Credentials become high-value targets

AI Fabrix Identity is delegated per request

What breaks traditionally

Elevated access persists indefinitely
Breach impact is systemic

Why Fabrix holds

Execution always occurs within user authority

6. AI Exception Paths

Traditional AI Stacks

AI Fabrix

Traditional AI Stacks AI requires special access rules

AI Fabrix AI follows the same rules as humans

Traditional AI Stacks “Temporary” exceptions become permanent

AI Fabrix No exception paths exist

Traditional AI Stacks Risk scales with intelligence

AI Fabrix Risk remains bounded

What breaks traditionally

AI deployments are blocked by security
Or worse, approved with unsafe exceptions

Why Fabrix holds

AI is a first-class governed actor

7. Integration Fragility

Traditional AI Stacks

AI Fabrix

Traditional AI Stacks Custom code per integration

AI Fabrix Declarative, standardized pipelines

Traditional AI Stacks Changes break downstream systems

AI Fabrix Changes are isolated structurally

Traditional AI Stacks Integrations are opaque

AI Fabrix Integrations are inspectable and auditable

What breaks traditionally

Integration maintenance dominates cost
AI initiatives slow over time

Why Fabrix holds

Integrations are part of the governed dataplane

8. Platform Sprawl

Traditional AI Stacks

AI Fabrix

Traditional AI Stacks Each team adopts its own AI tooling

AI Fabrix One enterprise AI operating model

Traditional AI Stacks Governance differs by team

AI Fabrix Governance scales automatically

Traditional AI Stacks Costs and risk multiply

AI Fabrix Costs and risk are predictable

What breaks traditionally

AI becomes unmanageable at scale
Enterprise architecture fragments

Why Fabrix holds

Standardization is structural, not enforced by policy

Summary: Where Failure Is Allowed

Traditional stacks fail when identity is dropped, policy is fragmented, or data access bypasses governance layers—creating compliance gaps and audit blind spots.

AI Fabrix succeeds because…

Identity and policy are enforced at every layer
Data access remains contextual and permission-aware
AI agents operate within the same governance model as humans

In-Tenant, Identity-Bound Architecture