Why Enterprises Must Prioritize LLM Data Control

Prioritizing data security is crucial when working with large-language models. Enterprises need to better understand the data control differences between public and private LLMs.

Shomron Jacob, Head of Applied Machine Learning and Platform, Iterate.ai

May 14, 2024

4 Min Read
Large Language Models - LLM - Artificial Intelligence Systems Capable of Understanding and Generating Human-like Language Based on Input Data
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Enterprises eager to harness the increasingly vast potential of large-language models (LLMs) have a defining choice to make: Select an available public LLM or deploy a private LLM all their own. 

Public LLMs -- such as GPT-4 and others -- are proprietary vendor offerings providing a model-as-a-service. This path comes with two key drawbacks. The first is that public LLMs will train on any data your enterprise makes available to them while offering those same advantages to competitors. This makes it difficult for generative-AI-powered applications to move the needle with differentiated results. 

The second (and related) drawback is data security. 

In the long term, enterprises that put generative AI at the core of their business offerings will chafe under the limitations of public LLMs. Providing services that stand apart in the marketplace requires full and direct control over your data and model, and AI that’s simply quicker, less expensive, and functionally superior to what generalized models can deliver. While the largest tech companies have the upper hand in providing AI solutions that “do everything,” enterprises leveraging private LLMs can thrive by offering solutions tuned to the needs of their specific addressable markets. Sustainable data sourcing and data security are essential if an enterprise is going to continually refine its model and outcompete its way to success. 

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Assign Data Security the Priority it Deserves 

Data security is the number one reason for enterprises to opt for a secure private LLM over a public model-as-a-service option. LLMs utilize vast troves of training data, from product information to customer profiles, ERP tracking information, camera vision, and more. In many ways, that data is the core strength of your enterprise, and ought to be protected as such. 

Here are six key areas of concern when it comes to LLMs and data security: 

1. Public LLMs put data at risk. As crucial as securing customer and financial data is to an organization, the lure of sending that data off to a public LLM for potentially beneficial analysis is as strong as it is dangerous. Case in point: Samsung was forced to restrict internal usage of ChatGPT last year after employees submitting proprietary data led to a data breach. In contrast, enlisting a private LLM offers the same advantages while preventing such leaks. 

2. Using a private LLM means you control the guardrails. Feeding internal data into an LLM requires careful guardrails to protect sensitive data -- financial information, employee data, etc. -- from exposure. In many cases, the risk of that data becoming exposed is too great to put security in the hands of an external party. 

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3. Be wary of external training sets. Enterprises may see an opportunity to jumpstart LLM training by buying external data sets. However, that strategy comes with significant risks. Such data may include inaccuracies, have issues with custodial ownership, or even be illegal under privacy laws in certain jurisdictions. For enterprises, sticking to homegrown data is, by a wide margin, a safer bet. 

4. Consider open-source versus proprietary LLM security. Enterprise LLM options include proprietary models such as GPT-4 and Gemini, and open-source models such as Llama and Mistral. The same conversation about open source versus proprietary approaches to security translates to the LLM space, with black-box, proprietary LLMs undergoing less scrutiny. Meanwhile, open-source models have their security continuously hardened by their larger open-source communities. 

5. Don’t adopt a model, adopt a platform. There’s risk in adopting a singular LLM -- especially a public LLM that may or may not remain reliable in the face of marketplace turmoil. Instead, by viewing AI from a platform perspective and strategically combining data, models, and integration points, enterprises can preserve their ability to switch to other LLMs if necessary. 

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6. Secure LLM hosting, IT and network security. Hosting, IT, and network security are key aspects of a secure LLM deployment. The best route for an enterprise to achieve that security is with a secure and directly controlled data center; the next best is to use a private cloud from a trusted provider. While public LLMs offer a shared cloud model that meets the vast storage and compute power LLMs require, that model also opens enterprises to potential security exposures. 

Unlock Your Full-Fledged LLM Potential Securely 

There’s little doubt left that an effective LLM strategy is becoming a competitive necessity for enterprises. But as organizations define their approaches in the marketplace, those that opt for private LLMs and prioritize security will thrive, and avoid obstacles known to trip up enterprises that fail to display the same foresight. 

About the Author(s)

Shomron Jacob

Head of Applied Machine Learning and Platform, Iterate.ai

Shomron Jacob is the Head of Applied Machine Learning and Platform at Iterate.ai. He began his career as a software engineer but soon found himself learning ML/AI, and switched his professional direction to follow it. He lives in Silicon Valley.

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