The Governed AI Framework™

The Governed AI Pyramid™, and the engagement that builds it.

Four layers in the order they are built, four phases that run them through an engagement, the Governed AI Loop™, and where each layer applies to your stack. This is the whole method, at the level a client can read before a call and a competitor cannot execute from.

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The Governed AI Pyramid™

Governed AI, built in four layers.

Protect the stack from the outside. Contain what the AI may see, say and do inside. Prove it works, live, in your environment. Augment your people with AI tools built for the work they already do. Each layer rests on the one below it, and leaving one out leaves the AI ungoverned, whatever the policy says.

Each layer rests on the one below it. Nothing holds if the stack is open; guardrails hold only on a secured stack; the proof runs only on what is contained; and the tools for your people sit on top of all of it.

Protectthe stack, from the outside

Can my AI be used against me?

No. The stack is hardened against attack from outside before any AI runs on it.

A private network with no public endpoints, identity federated to your directory, secrets out of code, the infrastructure patched and monitored, and the AI’s own doors closed: prompt injection, model theft, poisoned data, hijacked agents.

How you know: Posture evidence against named controls: your cloud’s well-architected security guidance and your AI platform’s security framework, with the threat model drawn from MITRE ATLAS.

Containguardrails, inside and out

Can my AI leak, or be turned from inside?

No. Guardrails hold what it may see, say and do, so nothing leaves a domain unrecorded.

Catalog permissions, row filters and column masks on what a model may see; gateway rules on what it may say; bounded authority on what an agent may do; egress controls between domains and to the outside. An insider with a prompt gets no more than their own access, and PII and IP stay where they belong.

How you know: The audit bundle for every workload, checked against the OWASP Top 10 for LLM Applications: what the model saw, what it answered, what it did, who asked, and what left the domain.

Provea proof of concept, live in your cloud

Does this work in my environment, or only on a slide?

In your environment. We build a proof of concept that runs the whole pyramid, live.

A real workload on real governed data, with lineage, audit and inference records end to end. You watch it stop a leak, record a decision and name its owner, scored before and after. Yours to keep.

How you know: The proof of concept’s evidence log, which an auditor can follow, and its score on the Earthonomy platform before and after, by validators independent of the team that built it.

AugmentAI tools for your workforce

What will it do for my people?

Give them AI tools built for the work they already do, and put those tools into production.

The first tool chosen by what it lets a person do that they could not before, built with a named human owner and bounded authority, productionized rather than piloted forever, with skills built on every deployment.

How you know: The augmentation map, output per person measured before and after, and the workforce impact published.

The Governed AI Loop™

Four layers, four phases.

Every engagement runs the four layers through four phases, and each phase earns the next. We call the cycle the Governed AI Loop™. The Governed AI Pilot™ is the first two turns; stewardship re-scores on a cadence and feeds the next assessment, so the loop never leaves anything running ungoverned.

Assessthe scored baseline, the risk register, the findings report
Provethe solution design and the proof of concept in your cloud
Scalethe design built out across your workloads
Stewardpatched, monitored, re-scored on your cadence

The Governed AI Loop™: Assess and Prove are the Governed AI Pilot™; Steward returns to Assess on the cadence you set.

The Governed AI Framework: what each layer of the pyramid produces in each phase of an engagement
Layer	1Assess	2Prove	3Scale	4Steward
Protectthe stack, from the outside	Posture against your cloud’s well-architected security guidance and your AI platform’s security framework; the threat model from MITRE ATLAS	The hardened workspace stood up in your cloud for the proof	Network, identity and secrets as code; every control mapped to its clause	Patched, monitored and re-scored on the cadence you set
Containguardrails, inside and out	What each AI system may see, say and do today, and where PII or IP could leave a domain, against the OWASP Top 10 for LLM Applications	Catalog permissions, masks and gateway rules proven on real governed data	Guardrails enforced in the path, bounded authority for every agent, egress controlled	Rules reviewed as workloads change; nothing new runs ungoverned
Provea proof of concept, live in your cloud	What the proof has to show in your environment; the workload and the data chosen; the evidence log begins	The proof of concept built and run on real governed data, with lineage, audit and inference records end to end, scored before and after	Every workload carries the same records: lineage, audit, inference, a named human per decision; monitoring of quality and drift	The audit bundle on every workload; independent validation; a verifiable credential
AugmentAI tools for your workforce	The augmentation map: the first tool chosen by what it lets a person do that they could not before	The tool used by the people it is for, on the proof; output per person measured	Tools productionized across teams; skills built with every deployment; workforce impact published	Output per person re-measured; the next tool chosen the same way

Where it applies

Cloud best practices around the workload. A governed platform managing the AI stack.

The pyramid maps to components, not to a vendor: your cloud’s network, identity and secrets; a governed catalog, model serving and a gateway; lineage, audit and inference records; and the interfaces your people use. We implement them on Azure, AWS or Google Cloud.

Your people
- A named human owns every decision that affects a person
- Output per person is the metric
- Appeal and reversal built in
AI workloads
- Assistants, agents and models, each with bounded authority
- Chosen for augmentation, not replacement
Your AI platform, managing the AI stack
- A governed catalog: who may see what, to the row and column, for data and models alike
- Model serving behind a gateway: prompt, output and rate guardrails
- Lineage, audit logs and inference records
- Monitoring of model quality and drift
Your cloud: Azure, AWS or Google Cloud
- Private network, no public IPs on compute
- Identity federated to your directory; access as code
- Secrets out of code; monitoring of the infrastructure and the AI
Protect Your cloud · Your AI platformContain Your AI platform · AI workloadsProve Your cloud · Your AI platform · AI workloads · Your peopleAugment AI workloads · Your people

Where each layer of the pyramid applies. The bands on the right show which parts of your stack each layer governs. The control mappings, the instrument and the blueprints behind this picture are delivered inside an engagement.

The platform we implement on. We implement this stack on Databricks: one governed platform, inside your own cloud, for the data, the models, the agents and the records they leave. How Databricks carries each layer

Checked against the frameworks your security team already uses.

The posture evidence names its controls, so nobody has to take our word for it.

MITRE ATLASthe catalog of attacks on AI systems the threat model is built from
OWASP Top 10 for LLM Applicationsthe application-level risks every guardrail is checked against
Your AI platform’s security frameworkthe controls for each component of the AI stack; on Databricks, the Databricks AI Security Framework
Your cloud’s well-architected security guidanceAzure, AWS or Google Cloud, for the network, identity and secrets
NIST AI RMF and ISO/IEC 42001the management system the evidence is organized for
EU AI Actthe regulatory clock, with every control mapped to the clause that asks for it

Augmentation, not replacement

AI that makes your people more capable is also the AI that is safe to run.

AI bought to cut headcount produces autonomous agents with broad access and nobody to answer for them. AI built to augment people keeps a named human on every decision, with bounded authority for agents. Smaller blast radius, cleaner audit, and the productivity gain shows up.

A named human answersEvery automated decision that affects a person has an owner who can explain it and reverse it. Agents act with bounded authority, never alone.
Output per person is the metricThe first tool is chosen, and the engagement is measured, by what each person can now do that they could not before. Headcount is not the metric.
Skills grow with every deploymentThe workplace adopts AI to extend what its people can do, and trains them to do more, not less.
Workforce impact is publishedAny headcount change made for AI is disclosed, so customers, investors and the workforce can choose with their eyes open.

What we promise, and what we do not. We never promise that a client will not change its staffing; that is the client’s decision. We promise the design and the disclosure: every system we build is designed to raise output per person, every decision has a human owner, and the workforce impact is published. An economy runs on wages, and AI aimed only at jobs breaks the first link.