GAI‑INFRA: Governable AI Infrastructure v1.1 (CAKE)

GAI INFRA Synthesizer — BeResponsibleAI
BeResponsibleAI Institute for Responsible Healthcare AI
GAI‑INFRA
Governable AI Infrastructure v1.1

A governable AI system spans energy to human authority.

Seven layers, six domains each, inside an institutional field. Governance is not the eighth layer — it is the filament threaded through all of it, and it only counts where a control, an owner, an authority and evidence can be named.

42
Domains
0
Attached
0/6
Capacity
AI Profiler passport · derived from this configuration 0 unresolved
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Operational risk tier
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Signature / configuration conflict

Field = condition. Cake = posture. The field states what is true of the environment; the cake states what this system does about it.

Purpose · Legitimacy · Human authority
Action / Outcome
Learning & Reassessment
Outcome governance → continue or stop

Capacity constrains governance; governance constrains the system. A control that is only specified is a gap, not a control — and Lapsed is reachable from any state.

Layer 5 · six structural domains

Knowledge & Intelligence Runtime

Inference is not authority.

L5 layer
5.6

Evaluation, Calibration & Validation

Multi-select
Implementations present

Consequence class — second axis on 6.2 CC1

The token list records what the system touches. This records what happens when it is wrong — severity, not action type. It cannot be inferred from the tokens, and it is the axis that most determines the derived tier.

L7 signature construct

The Human–System Capability Gap

When system capability exceeds verifiable human and institutional capability, effective governance can degrade even when formal controls remain present.

AI can erode the very human capability required to govern it — through deskilling, automation dependence, attention erosion, and reduced contestability.

Governance filament · attachment

Control → Owner → Authority → Evidence

5.6

Accountability recorded without authority — verify this is not a moral crumple zone.

Control maturity
RATSe™ pillars that bind here — not all apply, and that is meaningful
Cross-layer evaluation

Architecture Synthesizer

Architectural signature
Not yet analysed
Execution paradigm
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Agency level
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Findings

Configure the layers, then run the analysis. Findings describe configuration and gaps — they are not an assurance statement.

Cross-layer rule

Layers define primary architectural domains; real systems span several. RAG sits across L4–L5. An AI factory spans L1–L3. Radiology AI runs L4 through L7. Governance is different: it is not a cross-layer technology but the transverse filament operating across all of them.

RAG · L4↔L5 AI Factory · L1↔L3 Radiology AI · L4↔L7 Autonomous vehicle · L3↔L7

Six functional slices mapping the six governance functions across the entire AI lifecycle. Click a slice to explore.

AI Lifecycle
Governance

The crucial distinction: The Governance Pizza is not the RATSe™ Pizza.

There are two different sets of six:

Governance Pizza
HOW GOVERNANCE OPERATES

P.R.I.M.E. → AI Profiler → RATSe 1 → RATSe 2 → HCG → Outcome Governance

RATSe™ Pillars
WHAT GOVERNANCE PROTECTS

Stewardship | Accountability | Transparency | Safe/Secure/Resilient | Ethical/Equitable | Environmental

The RATSe™ six pillars operate inside RATSe™ 1 and RATSe™ 2, rather than becoming the six lifecycle slices. That is the clean architecture.

Created by Dr. Sharad Maheshwari MD — imagingsimplified@gmail.com
Founder: BeResponsibleAI & Institute For Responsible Healthcare AI
Inspired by Yann LeCun's AI Cake metaphor

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