GIAS dimension
Trust Boundaries · Governed Intelligence Architecture
Trust boundaries are the precise locations where responsibility, permissions, ownership, or control move from one actor, system, model, user, department, or institution to another. Ananke treats every boundary crossing as a governance event.
Boundary thesis
Trust is not assumed. Trust is evaluated at the point where authority changes hands. If a system cannot identify the boundary, it cannot reliably govern the decision.
Why trust boundaries matter
User ↔ Model
The model receives intent, but it does not inherit unrestricted authority from the user.
User ↔ Organization
Personal agency, institutional role, policy scope, and accountability must remain distinct.
Organization ↔ Provider
Model providers can supply inference. They should not own identity, governance, or institutional memory.
AI ↔ External Systems
Every tool call, API request, database change, or workflow action must cross an explicit execution boundary.
The architecture
Identity boundary
This boundary determines who the actor is, what authority they possess, and whether that authority can be delegated into an AI-mediated exchange.
Data boundary
This boundary determines who owns the information, whether it may move, where it may be stored, and which systems may use it.
Governance boundary
Risk scoring, compliance checks, age-aware controls, permission evaluation, and escalation rules determine whether intent becomes authorized action.
Execution boundary
Tool invocation, API calls, database changes, workflow triggers, and external system actions must execute only within approved scope.
Persistence boundary
Memory storage, retention policies, audit preservation, deletion rights, and evidence retention determine what becomes part of the system’s future state.
Boundary principle
Models generate possibilities. Governance determines authority. Execution performs approved actions. Forensic replay preserves accountability through reconstructable evidence.
Identity
Who is acting?
Context
What matters now?
Governance
What is allowed?
Model
What is possible?
Execution
What is performed?
Audit
What can be proven?
Boundary map
Human ↔ AI
Every human-to-AI exchange must preserve intent, consent, scope, and accountability.
Parent ↔ Child
Age, guardianship, safety policies, and delegation require explicit boundary evaluation.
Employee ↔ Organization
Role, department, access level, institutional policy, and audit obligation shape authority.
Organization ↔ Vendor
Data handling, model routing, provider choice, and liability must remain governed.
AI ↔ Tool
Tool invocation moves from language into action and must pass policy, permission, and scope checks.
AI ↔ AI
Model-to-model exchanges require chain-of-custody, authority containment, and traceable context transfer.
Rita + Palladium + Origin
Rita implements the trust-boundary model defined by GIAS, translating architectural principles into authority chains, policy language, institutional rules, and executable decision logic.
Understand Rita
Palladium operationalizes trust-boundary evaluation for organizations through routing, policy execution, evidence recording, containment, and forensic replay.
Explore Palladium
Origin applies governed trust boundaries to the individual, preserving identity, memory, consent, continuity, and personal rights above any single model provider.
Explore OriginArchitecture pages
Control Surfaces
Where decisions can be made, authority must be explicit.
Threat Model
The risks governed intelligence must defend against.
Forensic Replay
Every significant decision should be reconstructable.
GIAS Overview
Return to the architectural overview and explore how the dimensions fit together.
Next: Control Surfaces
Trust boundaries identify where authority changes hands. Control surfaces define where decisions can be shaped, constrained, approved, escalated, or audited before AI-driven action occurs.
