Content: Blog

Securely Connecting Your EU Enterprise Data To AI

Executive Summary

NVIDIA‘s recent introduction of NEMO Retriever represents a significant stride in AI technology, offering enterprise-grade data connectors specifically designed for Large Language Models (LLMs).

The integration of sensitive and private company data necessitates adherence to corporate data and security standards. With NeMo’s Retriever, protection is paramount; not only securing data but also enhancing the accuracy of AI responses, all the more accurate and relevant when leveraging targeted data sets.

As a trusted NVIDIA Solution Provider and NVIDIA DGX Cloud Service Provider, NEBUL is at the forefront of implementing NVIDIA’s AI Enterprise Software Suite.

This comprehensive AI Software Stack is the foundation upon which NEBUL builds enterprise-grade AI infrastructure and cloud solutions.

By integrating these elements into a holistic solution, NEBUL and NVIDIA ensure that enterprises are equipped to leverage the full potential of AI while maintaining an uncompromising stance on data integrity and security.

Experimenting with Public AI Tools

In the corporate setting, artificial intelligence interfaces such as OpenAI’s ChatGPT, Google Gemini, AWS Bedrock and Microsoft CoPilot are becoming household names. These advanced technologies have transcended mere recognition; they are now actively being integrated by professionals at various levels to amplify productivity and enhance output. The adoption of AI tools is not just a trend but a transformation in how we approach tasks, streamlining workflows and sparking innovation on both individual and organizational scales.

Sensitive, Private Data is Leaking from Public AI Platforms

A pressing concern has surfaced when using public AI platforms: the inadvertent leakage of sensitive data. As AI models are typically trained on vast, indiscriminate datasets, they may not always discern the ‘exact’ truth, occasionally sourcing responses from unverified internet content.

The gravity of the situation is compounded by the alarming ease with which unauthorized and proprietary information can be extracted through rudimentary methods. This vulnerability exposes individuals and corporations to potential data breaches, underscoring the urgent need for fortified data handling protocols within AI platforms.

Companies are Banning ChatGPT and Other Public AI Services from Internal Use

We are staring into the sunrise of the AI revolution, where the integration of AI tools into business processes is still like a toddler learning to walk. A discernible shift is emerging as organizations grapple with the implications of external AI tools access to proprietary data.

There has been a naive rush to upload sensitive assets – software code, engineering blueprints, and employee details onto blossoming AI platforms, but the truth is that huge risks are involved when it comes to protecting the exact data needed to make AI useful.

For the interim, while navigating these uncharted waters, it’s wise to proceed with extreme caution:

1. Refrain from providing Public AI interfaces like ChatGPT with proprietary company data.

2. Avoid entrusting AI tools with access to sensitive login credentials for email, calendars, and associated datasets.

The temptation to leverage cutting-edge AI must be balanced against the safeguarding internal corporate and personnel data.

The inner workings of Large Language Models (LLMs) and their data processing methods remain a complex enigma, often not fully grasped even by the creators of these technologies.

As it stands, the race towards innovation and feature expansion by AI developers can sometimes outpace the necessary focus on cybersecurity and data. It’s crucial to remain vigilant and prioritize data protection over the allure of novel AI functionalities.

Introducing Private AI

Private AI represents a shift to safety, where enterprises can harness their own AI solutions, tightly integrated with their proprietary data, yet isolated from the external digital world and eliminate associated risks. This AI can be hosted within a company’s private data center or through a trusted enterprise cloud provider on secure, dedicated infrastructure.

In essence, Private AI can operate exclusively on an organization’s internal data and internally hosted pre-trained GPTs, tailored to deliver outcomes that align with the company’s strategic goals.

Imagine an AI system that has absorbed the entirety of a company’s tech support interactions to enhance problem resolution processes. Or an AI that interacts with a repository of financial records, design better products and empowering employees to discover novel strategies that bolster efficiency and fortify the company’s competitive edge. It’s clear that companies must forge ahead, but guardrails are needed for now to protect company assets.

A Future Vision of AI in the Corporate Landscape

The ultimate pinnacle of AI adoption in the corporate realm is the comprehensive integration of corporate data into AI-centric systems, such as embedded data in vector Databases. This integration enables AI to apply machine learning and generative learning techniques to produce desired outcomes effectively.

This sudden reality is a seismic shift in IT security and data security and data management processes. As AI’s full potential hinges on unfettered access to our data, we must swiftly and fundamentally reconceptualize our approach to safeguarding and structuring our data ecosystems.

Shadow AI is Real and It’s Happening Now

In a similar vein to the early days of cloud computing, when developers utilized public clouds before robust security measures were established, we’re now witnessing the rise of Shadow AI. In this scenario, employees are bypassing formal approval processes to employ AI tools for work-related tasks, often uploading sensitive company data and documents without proper authorization.

Companies today must be clear with their policies, and above all offer reasonable alternatives which are safe and secure.

Private AI and RAG (Retrieval Augmented Generation)

AI technology, as advanced as it seems today, is just beginning to tap into its full potential. Leveraging AI to connect with and process specific datasets for tailored outcomes is still at the forefront of innovation.

Across various roles, from executives to engineers coding, marketers crafting content, tech support providing resolutions, to customer service deploying chatbots — customized AI applications are transforming daily work tasks.

‘Retrieval Augmented Generation’ (RAG) is the technical term for the integration of YOUR data with AI capabilities. This process enhances AI functionality by accessing and utilizing your unique datasets, allowing for more relevant and useful outputs.

While there’s an abundance of commercial startups and open-source tools that claim to offer seamless data alignment and impressive results, the lingering question is their security. Ensuring the confidentiality and integrity of data in these innovative AI applications is paramount and remains a critical concern for the industry.

Enterprise Grade RAG with LLMs

The integration of Large Language Models (LLMs) into business environments, particularly with a focus on security, is a critical aspect of modern AI deployment. NVIDIA’s introduction of NVIDIA NeMo Retriever as an extension to NeMo, specifically designed for enterprise users, is a significant advancement in this field. This add-on allows companies to harness the power of LLMs while maintaining strict control over their sensitive data.

Here’s an overview of the benefits of NVIDIA NeMo Retriever:

Enhanced AI Performance Comparable to Public Models

NeMo Retriever ensures that enterprises do not compromise on the quality of AI services. The performance of these privately hosted LLMs matches or surpasses that of public models, offering robust and efficient AI solutions.

Increased Data Accuracy with Reduced Hallucinations

A notable advantage of NeMo Retriever is its ability to provide more accurate data responses. By reducing the incidence of ‘hallucinations’ (inaccurate or irrelevant information generated by AI), it ensures that the output is reliable and relevant to the task.

Robust API Support and Stability

NeMo Retriever offers comprehensive API support, ensuring seamless integration and interaction with various enterprise systems. This stability is crucial for businesses relying on consistent and uninterrupted AI services.

Security-First Approach

With a focus on security, NeMo Retriever prioritizes the protection of sensitive data. This approach is essential for enterprises handling confidential information, as it mitigates the risks associated with data breaches and unauthorized access.

Regular Security Patch Releases

Staying ahead of potential vulnerabilities, NeMo Retriever is maintained with regular releases of security patches. This proactive stance on security ensures that the system remains safeguarded against emerging threats.

Dedicated Enterprise Support

NEBUL and NVIDIA provides specialized enterprise support for NeMo Retriever users. This ensures that businesses have access to expert assistance and guidance, enhancing the overall user experience and efficiency of the tool.

Versatile Connectivity with Various Data Types

NeMo Retriever is designed to connect private AI engines to a wide range of file types, including text and images of any format. Its roadmap includes expanding support to encompass all popular databases and data types, highlighting its versatility and adaptability to different enterprise needs.

NVIDIA’s NeMo Retriever represents a significant step forward in the enterprise application of LLMs, delivering a secure, accurate, and robust AI solution that caters to the specific needs of businesses while safeguarding their sensitive data.

NEBUL’s Private AI Delivers NeMo and NeMo Retriever in our Private AI Cloud or in Your Data Center

NEBUL’s collaboration with NVIDIA, leveraging the advanced capabilities of NeMo Retriever within the NVIDIA AI Enterprise software stack, positions it at the forefront of Private AI cloud and infrastructure solutions.

NEBUL, as a Private AI cloud and infrastructure solution provider, integrates the cutting-edge NVIDIA NeMo Retriever into its services, making it readily available to all customers and companies exploring AI applications in their domains.

By deploying NEBUL’s Private AI, corporate data is secured within isolated environments, powered by high-end NVIDIA GPUs, networking, and storage, with a primary emphasis on security over risk.

NVIDIA’s strategy of embracing both open-source and commercial software within its AI Enterprise software stack enables Cloud Service Providers like NEBUL to swiftly deliver the latest enterprise software and security updates to their customers.

NEBUL offers these complete Private AI services in a turn-key ‘cloud’ format, enabling rapid deployment and flexibility, whether hosted in NEBUL’s cloud or on-premises within a customer’s data center. This approach aligns with the swift, secure, and scalable deployment needs of modern enterprises.

Next Steps to Enabling Generative AI in a Corporate Environment

With the advent of enterprise-grade retrieval-augmentation solutions, organizations finally have the opportunity to develop LLMs tailored to their sensitive corporate data, paving the way for broader and more strategic AI adoption.

The journey towards integrating AI is not just inevitable but essential for any company to maintain a competitive edge.

It’s critical, however, to embark on this path with a dual focus; fostering innovation and ensuring compliance with data protection policies that align with organizational best practices, along with delivering this in a format users can adopt.

NEBUL stands at the ready to secure and operationalize your Private AI initiatives. We invite you to engage with us in a conversation that could redefine the future of your enterprise’s AI journey.

Let’s connect and explore how NEBUL can empower your business with secure, cutting-edge AI solutions, on-prem or cloud.

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