Private AI Networks for Enterprises

Private AI Networks for Enterprises

266 million companies worldwide are using or exploring AI in their business operations. 72% of those businesses believed AI would have an impact on their business within 5 years. Large Language Models (LLMs) and AI Agents are the core components of AI.

These models, when used improperly, can introduce great risks. Main concerns are how data is used to train the models and how models are protected after ingesting that data. In October 2023, the US administration issued an executive order to address “safe, secure, and trustworthy AI.” The fact sheet prioritizes usage and development of privacy enhancing technologies (PETs) to “protect Americans’ privacy.” 

Private AI Networks serve as a framework for safe, secured and trustworthy AI. Let’s dive deeper into that topic and describe the core principles of those.

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What is a Private AI Network?

Today AI can help enterprises to make the most of their data generated from various business cases constituting core business.

However, AI models suffer from insufficient transparency of where the data goes and how is used. Private, sensitive information could be shared with third parties or used in AI training.

With proper private AI architecture implemented, queries are restricted to address company’s internal databases, API and numerous enterprise private data sources. This approach could incorporate using Retrieval-Augmented Generation (RAG) to securely interact LLMs and provide enterprise specific results.

Implementing Private AI, enterprises can exploit benefits of LLMs while guaranteeing the safety of their data sources.  

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Private and Public AI Networks compared

The whole digital world is experiencing AI transformation along with big changes in how people interact with it. AI is moving beyond narrow, specialized models and towards development of highly capable, Autonomous AI Agents that can truly augment and enhance human intelligence across a wide range of domains.

Public LLMs have been trained on public data sets as well as continue to use new data that users submit into their models.

A challenge arises for enterprises that streamline their AI efforts but concerned about keeping their IP and data sources private. Data retrieved from user queries against publilc LLMs is stored in third party databases and made available to third party providers. The biggest risks for enterprises when using public LLMs are as follows.

Data security and privacy           

Data breaches, unauthorized access or data misuse.

Latency and Real-time Processing    

Due to network latency and resource constraints, real-time processing is problematic for many applications.

Scalability and cost

Cloud providers charge exorbitant fees for the required infrastructure, making it difficult for organizations to adopt.

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Enterprises that prioritize the value of keeping data safe are approaching solutions that ensure privacy, control and efficiency. Private AI Network is a strong trend towards implementation of such solutions.

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Reasons to adopt Private AI Networks by Enterprises

There are several core reasons to adopt Private AI Networks architecture at an enterprise level.

• Data privacy

Enterprises keep sensitive information safe a way utilizing private data sources and local LLMs.

• Speed and ease of workflow

Using Private AI Networks, enterprises can easily retrieve confidential information from internal data sources quickly and efficiently through existing automated workflow processes.

• Control

Private AI Networks offer to Enterprises increased granularity in control over how private data sources are used and accessed utilizing Role Based Access Control (RBAC) permissions. Instead of sending private and IP related data to public LLMs with the potential for exposure to third parties, enterprises can keep an eye over their data.

• Competition trend setting

By adopting Private AI Networks, enterprises can build domain-specific LLMs trained on internal data, further enhancing knowledge transfer within their core businesses.

• Regulatory compliance

By keeping sensitive information under control and managing how to use it, enterprises can ensure they are compliant with privacy laws and regulations.

Health Insurance Portability and Accountability Act (HIPAA), California Consumer Privacy Act (CCPA), General Data Protection Regulation (GDPR) mandate strict requirements for data control and usage.

• Customer trust and satisfaction

Enterprises can substantially boost client acquisition rates and customer trust by increasing data privacy through Private AI Networks.

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Private AI Networks solution

 Launching and scaling Private AI is a multi-step and straightforward process.

• Reviewing existing data pipelines

Enterprises should review their private data sources and data lakes. Prepare required modernization (if required) to be ready to automate data retrieval and ingestion via automated workflow processes. As a result of modernization (harmonization) enhancements, data should be clean, consistent and delivered to consumers in real time.

• Computation environment

Private AI Networks require high-performance computing (HPC) infrastructure for AI applications. High Density Colocation environments would fit the requirements provided power, security, cooling and regulatory compliance are addressed accordingly. Data gravity concerns can be mitigated by applying a distributed approach to process data at the Edge. In the meantime, Edge data is delivered to Enterprise data lakes via automated pipelines. Via appropriate data transformers, adapters and APIs, Enterprise data lake is connected to private LLMs. Retrieval-Augmented Generation (RAG) approach is exploited to build Private Enterprise “data knowledge hub”.

• Creating an implementation team

In-house engineering team to collect and prepare data pipelines and automated workflows for Private AI Networks.

DevSecOps team to build development-stage-production environments to develop, test and publish into production.

The right ecosystem of partners should be built to leverage integrations of third party solutions with in-house products.

Software team to develop core components and integrations with partners solution.

Expert matter team to plan and control implementation for Enterprise business cases.

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           SpartanShield Private AI Networks Platform

SpartanShield Private AI Router Platform can be helpful in delivering the following core components of Enterprise Private AI.

• HPC and Edge resource inventory (Resource Inventory)

Various types of resources are maintained in the inventory – servers, gateways, routers, laptops, mobile devices, sensors and others – to schedule provisioning of LLMs to in-house computation environments.

• LLMs inventory (Model Inventory)

Due to restrictive software licenses applicable to close sourced LLMs, the inventory is based mainly on open-source models.  The models are analyzed against the existing Resource Inventory for automated provisioning to matched resources.

• Automated provisioning of LLMs to the matched resources.

• Private AI Networks creation along with Role Based Permissions (RBAC), users, groups and security assignments.

• Routing all traffic for in-house Private AI Networks.

• In-house gateway for coordination of outside requests to in-house Private AI Networks.

• Secured P2P tunnels for all traffic between in-house Private AI Networks and outside consumers.

• Customizable AI Agents for autonomous processing of business tasks.

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Future of Enterprise AI

As AI solutions become ubiquitous, private AI Networks is making a substantial impact on the way forward for Enterprises to prioritize data privacy and keep their business assets, IP and customers safe.

SpartanShield team is seeing the evolution of enterprise AI before our eyes now and here.