Risks and Solutions for Using Large Language Models in the Enterprise

Connected dots abstract representing large language models

Data governance is a significant concern for companies looking to utilize the immense power and possibilities of publicly available large language models and other generative AI tools – including ChatGPT and Google Bard. Advancements in modern-day AI used in enterprises cannot be taken lightly. Many data risks and security measures must be addressed before any implementation occurs.

The positive news is that large language models (LLMs) integrated with existing knowledge management systems can offer a secure way to take advantage of innovative content-generating features.

This article covers the data governance risks that companies may encounter when utilizing public generative AI tools. It offers insight into combatting these challenges by bringing the models inside their existing and secure platforms.

Data Security Risks of Publicly Available LLMs and Generative AI

Data Privacy: Generative AI tools often require access to large datasets to learn patterns and generate accurate outputs. Sharing proprietary or customer data with public AI tools raises concerns about data privacy. Companies must evaluate AI tool providers’ privacy policies and practices to ensure compliance with applicable data protection regulations. Data usage limitations must be in place to protect individual’s privacy rights and confidential company data.

Intellectual Property Protection: Public generative AI tools may inadvertently generate content infringing on intellectual property rights. In other words, the source is not identified by these tools, and this causes concern for copyright infringement and unknown accuracy of the outputs produced by the large language models – building reason for potential legal disputes and liability across the enterprise. To mitigate this risk, companies should thoroughly review the terms of use and licensing agreements of AI tools and implement validation of source information.

Biased Outputs and Reputation Risks: Public generative AI tools learn from data across the web. We all know that every corner of the web is not factual and can be harmful, opening the risk to biases and bigotry in the training data. Biased, offensive, or hallucinated outputs could damage or harm reputations. Companies must assess ethical ramifications and administer corrective measures to minimize biases and ensure responsible AI deployment.

Regulatory Compliance: Companies operating in regulated industries face additional challenges when using public generative AI tools. Compliance with industry-specific regulations may conflict with public AI tools’ usage terms or data handling practices. Companies must conduct due diligence and have proper systems and controls to ensure compliance with necessary regulations when leveraging generative AI tools and large language models.

Data Hallucination: Data hallucination occurs when an answer provided by an AI system appears valid but is inconsistent with the training data. The responses are difficult to spot because they come from generally trustworthy model predictions that produce unrealistic outputs. Data hallucination causes many headaches for organizations – for example, decision-making based on misinformation, legal implications, additional time spent verifying answers, and overall trust in artificial intelligence.

The Solution for Secure and Productive LLM Use

Integrating Large Language Models into Knowledge Management Platforms

New language models integrated into existing knowledge management platforms, such as Mindbreeze InSpire, are not only a way to reduce data governance risks but also provide users with much more relevant information in their specific roles. A knowledge management platform captures, stores, and manages data and information within a company. Integrating a language model would use pre-trained models trained on millions of data.

The first benefit of bringing language model’s capabilities into these platforms is the ability to ask questions in natural language, just like a publicly trained model, but receive answers generated from enterprise data – a way more helpful way for a user to get relevant information for a company-specific project. Secondly is validation and access to source information. Users can see which file or document the output was generated from, giving them access to more details and knowledge from the corresponding document or file.

In addition, enterprises can adjust access rights on their terms and take data permissions into account with full compliance.

Large Language Models

Integration enhances natural language querying, provides access to relevant and validated answers from enterprise data, ensures user context and data permissions, and supports semantic understanding and similarity for improved user experience and efficiency – a safe and secure way to use large language models inside your organization.


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