Taming the Chaos: Introducing the EAIS™ Framework for Governance and Control

The AI landscape is a chaotic gold rush of powerful tools. Solutions from OpenAI, Nvidia, Microsoft, Anthropic, and Google, alongside frameworks like n8n, Joule (SAP), Copilot (Microsoft), Duet (Google), CUDA (Nvidia), and others, all promise transformation. However, many of these vendor tools position themselves as 'general' AI solutions while remaining centered within their vendor boundaries.

But for the enterprise leader, this chaos brings unacceptable risk.

How do you control your proprietary data? How do you ensure compliance with internal policies and external regulations? How do you build a lasting, strategic advantage instead of just chasing the latest trend?

Decades ago, network engineering faced a similar chaos. The industry brought clarity by creating a common language and a layered structure to reason about complex systems.

The Open Systems Interconnection (OSI) model gave us a way to tame the complexity. It succeeded not because it was a perfect representation of every network, but because it provided a powerful framework for thinking.

AI has now reached its OSI moment. To harness its power safely and effectively, we need a new framework. A model that puts everything in its right place.^*

This is the EAIS™ (Enterprise AI Stack), a structured, layered approach for building secure, governable, and powerful AI solutions.

A Real-World Challenge: Production Line Down

To understand the EAIS™, let's start with a real-world problem. Imagine a production line is down. The maintenance technician needs to find the schematics for a specific part, check the maintenance history, draft a purchase order, and get it approved.

Without a structured AI model, leaders can't reason about AI's role in the enterprise. With the EAIS™, this becomes a secure, accelerated, and fully auditable workflow. Even with products from multiple vendors, the EAIS™ provides a unified framework for governance and control. Let's see how.

The EAIS™ at a Glance: Three Zones of Control

The EAIS™ is a comprehensive 8-layer model, but it's best understood as three distinct logical zones. This structure allows us to separate business concerns from technical implementation, giving clarity to everyone from the CEO to the enterprise architect.

• The Governance Workflow: The top layers that interface with the business. This is where business rules, human approvals, and real-world consequences live.
• The Orchestration & Control Plane: The "brain" of the operation. This central hub connects the business rules to the technology, ensuring security, logging, and access control.
• The Model Core: The foundational technology engine that provides the raw AI capabilities.

The EAIS™ in Action: A Play-by-Play

Let's walk through our maintenance technician's problem, seeing how each layer of the EAIS™ contributes to a secure and efficient solution.

Layer 8: ✅ Approved Action

This is the final, real-world outcome. The approved purchase order is sent, the part is shipped, and the production line is on its way to being repaired. This is the business value we are driving towards.

Layer 7: ✍️ Approval & Mandate

Before any action is taken, it must be validated. This layer represents the business rules. The system checks if the maintenance technician has the authority to order a part of this value. The workflow can prompt a supervisor for approval on high-cost parts or use a pre-approved mandate for routine orders, allowing for controlled autonomy.

Layer 6: 📝 Action Draft

Guided by the maintenance technician, the AI drafts a purchase requisition. It identifies the correct vendor, and prepares it for the approval workflow in Layer 7.

Layer 5: ↔️ Sources & Destinations

To create the draft, the AI needs information. This layer represents all the inputs and outputs of the system. In our story, the sources include the maintenance technician's prompt ("Find the part to fix this machine"), the company's ERP system, private retrieval-augmented generation (RAG) database (containing part schematics and maintenance logs), and even a Git repository (tracking infrastructure as code and software changes on the machine). The destination is the ERP system that will receive the purchase requisition.

Layer 4: 🧠 Orchestration

This layer is the stack's central nervous system. The Orchestration Layer takes the maintenance technician's simple request and:

• Verifies Identity: Confirms who the user is and what they are allowed to see.
• Enriches the Prompt: Securely queries the Sources in Layer 5, pulling only the authorized schematics and logs. It enriches the simple prompt with this deep, proprietary context.
• Controls Tools: It can securely use a Model Context Protocol (MCP) tool to check a vendor's live stock online, ensuring all tool usage is centrally logged and compliant with policy.
• Assembles the Final Prompt: It combines everything into a master prompt, ready for the AI engine.

Layer 3: 🚪 API & Containment Layer

This layer is both the front door and the factory that packages raw AI into a safe, reusable service. It exposes a well-defined service endpoint, whether OpenAI-style HTTP, Anthropic, or an internal gateway, so the rest of the stack can call the model. Under the hood, the neural-net weights are wrapped by an inference engine such as ONNX Runtime, TensorRT, or a custom server. This wrapper enforces governance (auth / rate-limit / logging) and provides an API for downstream layers. Here we might find CUDA-accelerated kernels handling the heavy math.

Layer 2: ⚙️ Neural Net

Sitting behind the service API is the core intellectual property: the trained Neural Net itself. This is the complex, unexplainable "black box" of weights and logic that does the actual reasoning. This could be within a public model like GPT-4 or a fine-tuned, specialized model that your organization has trained or re-trained for a specific task. File formats for the weights and model graphs found at this layer include PyTorch .bin, TensorFlow .ckpt, ONNX .onnx, or GGUF files.

Layer 1: 🌐 Infrastructure & Hosting

This is where the Neural Net physically runs. This layer represents a fundamental business decision. Is the model running on Public Cloud Infrastructure offered by a major vendor? Or, for ultimate privacy and control, is it running on Self-Hosted Hardware in your own private cloud or data center? Here we find hardware accelerators such as Nvidia's H100 (using CUDA drivers), and CPUs such as Intel's Sapphire Rapids.

The maintenance technician gets the right answer, fast. And the entire interaction has been logged, monitored, and compliant with company policies.

The Payoff: Clarity, Control, and Confidence

Adopting a structured model like the EAIS™ moves you from chaos to control.

Clarity

Everything has a place. You know where to implement access controls (Layers 4 & 5), where to define business rules (Layer 7), and where to plug in a new, more powerful language model (Layer 2) without disrupting your governance framework.

Control

The Orchestration Layer gives you fine-grained, centralized control over a decentralized world of tools and data. It allows you to embrace powerful standards like Model Context Protocol (MCP), giving AI a universe of tools, while ensuring every single action is logged and aligned with your policies, even on remote user devices.

Confidence

This model provides the foundation to deploy advanced concepts like autonomous agents and secure data silos with confidence. You know you have the governance framework to manage them, allowing you to innovate safely.

Build on a Solid Foundation

Stop chasing disconnected AI tools. Start building a unified, governable AI foundation that will serve your organization for the next decade. The EAIS™ provides the blueprint.

This article is the beginning of a conversation. In the coming weeks, I'll be publishing deep dives on each component of the EAIS™, including the Model Context Protocol (MCP) proxy, GitOps integration, and strategies for building secure data silos.

To continue the journey, follow me on LinkedIn and subscribe to the Yttrigen blog.