Unveiling how to Reduce Data Risk in the Company for Generative AI and LLMs

Businesses have swiftly realized the potential of generative AI to generate novel concepts and boost productivity among developers and non-developers. However, putting private and confidential information into large language models (LLMs) that are publicly hosted raises substantial security, privacy, and governance problems. They must handle these hazards before businesses can reap the rewards of these potent new technologies.

Businesses have valid worries that LLMs may "learn" from their prompts and provide confidential information to rival companies that submit identical questions. Businesses are also concerned that their private information might be kept online, exposed to hackers, or unintentionally disclosed to the public. That makes it impossible for most businesses, especially those in regulated industries, to feed data and prompts into publicly hosted LLMs.

Bring the LLM to your data rather than sending it to them. Most businesses will follow this strategy to strike a compromise between the necessity for innovation and the value of protecting consumer PII and other sensitive data. Large enterprises should host and implement LLMs within the secure environment that most currently maintain around their data in terms of security and control. This enables interaction between workers and the LLM and allows data teams to expand and personalize it while maintaining the organization's security perimeter. A good data strategy is a prerequisite for a successful AI strategy. This entails removing silos and establishing clear, standardized procedures that enable teams to access the required data while maintaining high security and governance. The ultimate aim is to have reliable data that can be used with an LLM and is easy to retrieve in a controlled setting.

LLMs trained on the whole internet pose issues beyond simply privacy. They can perpetuate prejudices, cause rude responses, and are prone to "hallucinations" and other mistakes, increasing the risk for firms.

Furthermore, fundamental LLMs cannot respond to inquiries that are particular to your company, its clients, or perhaps even your industry since they have not had access to your company's internal systems and data. The solution is to enhance and alter a model to be knowledgeable about your industry. Although hosted models like ChatGPT have received the most attention, there is a lengthy and expanding list of LLMs that businesses may download, personalize, and use behind the firewall. These include open-source models like StarCoder from Hugging Face and StableLM from Stability AI. Large quantities of data and computer power are needed to fine-tune a fundamental model for the whole web. Still, according to IDC, "once a generative model is trained, it can be 'fine-tuned' for a particular content domain with much less data."

A limited LLM can still be beneficial. For every AI model, "garbage in, garbage out" applies; thus, businesses should use internal data they are confident in and will provide them the insights they need to personalize models. Most likely, your staff won't need to ask your LLM for Father's Day gift suggestions or instructions on how to bake a quiche. However, they could wish to enquire about sales in the Northwest or the advantages of a certain customer's contract. The LLM will be tuned using your data in a controlled and secure setting to provide such responses.

Besides producing better outcomes, improving LLMs for your company can also help you use fewer resources. Unlike models created for general-purpose use cases or a vast range of business use cases across various verticals and sectors, smaller models addressing specialized use cases in the enterprise typically demand fewer computational resources and lower memory sizes. You may run LLMs more affordably and effectively by focusing them on the use cases in your company.  The information that may be beneficial for tuning a model on your internal systems and data must all be accessible, and most of this information will be kept in formats other than text. Unstructured data is around 80% of all data worldwide, including business data like emails, photos, contracts, and training videos. To build and train multimodal AI models that can recognize relationships between various types of data and surface these insights for your business, it is necessary to use technologies like natural language processing to extract information from unstructured sources and make it accessible to your data scientists.

Businesses must exercise caution with whichever strategy they adopt to use generative AI because this field is rapidly evolving. That entails reading the small print about the services and models they employ and working with reliable vendors that provide clear assurances regarding the models they supply. Companies, however, cannot afford to remain idly idling, and every company should be investigating how AI may revolutionize its sector. Risk and reward must be balanced, and you're more likely to benefit from the new opportunities this new technology offers by putting generative AI models near your data and operating within your current security perimeter.