Enterprises have started to realize the potential of generative AI in uncovering new ideas and boosting productivity. However, there are significant risks associated with deploying sensitive and proprietary data into publicly hosted large language models (LLMs). Security, privacy, and governance concerns have become primary obstacles for businesses looking to leverage these powerful technologies.

One of the main concerns is the possibility of LLMs “learning” from enterprise prompts and disclosing proprietary information to other businesses using similar prompts. Enterprises are also worried about the potential storage of sensitive data shared with LLMs online, which could make it vulnerable to hackers or accidental public exposure. As a result, many businesses, especially those operating in regulated industries, find it untenable to feed data and prompts into publicly hosted LLMs.

To mitigate the risks associated with publicly hosted LLMs, enterprises are adopting a different approach – bringing the LLMs to their data. This model allows enterprises to strike a balance between innovation and data security by hosting and deploying LLMs within their existing security perimeters. By maintaining a strong security and governance boundary around their data, businesses can further develop and customize LLMs while enabling employees to interact with them safely.

A robust AI strategy necessitates a strong underlying data strategy. This means eliminating data silos and establishing simple, consistent policies that facilitate team access to data within a secure environment. The ultimate goal is to have actionable and trustworthy data that can be easily used in conjunction with LLMs under a secure and governed setup.

LLMs trained on the entirety of the web bring privacy challenges and are susceptible to inaccuracies, biases, and offensive responses. These models may not have been exposed to an organization’s internal systems and data, making it difficult for them to provide specific answers tailored to a company’s unique business requirements and customer base.

To overcome these challenges, enterprises can extend and customize existing models to make them smarter about their own business. In addition to well-known hosted models like ChatGPT, there is a growing list of LLMs that businesses can download, customize, and use behind their firewalls. Open-source models such as StarCoder from Hugging Face and StableLM from Stability AI offer customization possibilities that organizations can leverage. Fine-tuning a foundational model with internal data requires fewer resources than training a model from scratch on the entire web.

An LLM doesn’t have to be vast to be useful. Customization is crucial to ensure an LLM provides relevant and accurate results. By tuning LLMs with internal data, enterprises can target specific use cases and reduce resource needs. Smaller models designed for particular enterprise use cases typically require less compute power and memory compared to generalized LLMs. This targeted approach to LLM development enables businesses to run LLMs in a more cost-effective and efficient manner.

Tuning a model on an organization’s internal systems and data requires access to a wide range of information, much of which is stored in unstructured formats. Approximately 80% of the world’s data is unstructured, consisting of emails, images, contracts, or training videos. Extracting valuable insights from unstructured sources requires technologies like natural language processing (NLP). NLP enables the extraction of information from various unstructured data sources and empowers data scientists to build and train multimodal AI models that can identify relationships between different data types.

The field of generative AI is rapidly evolving, and businesses must approach it with caution. It is essential to thoroughly read the fine print concerning the models and services used and collaborate with reputable vendors that provide explicit guarantees about their models. While there are risks involved, companies cannot afford to remain stagnant in the face of AI disruption. Striking a balance between risk and reward is crucial, and by bringing generative AI models closer to their data and operating within existing security perimeters, businesses are more likely to capitalize on the opportunities presented by this transformative technology.

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