Maximizing the Benefits of Generative AI: Balancing Innovation and Security

Enterprises across various industries have rapidly acknowledged the potential of generative AI to unlock new ideas and enhance productivity for both developers and non-developers. However, leveraging publicly hosted large language models (LLMs) introduces significant risks in terms of security, privacy, and governance. Companies need to address these risks before they can fully harness the benefits of these powerful technologies.

One of the primary concerns surrounding LLMs is the possibility of these models “learning” from prompts and inadvertently exposing proprietary information to other businesses that input similar prompts. Furthermore, businesses are apprehensive about sharing sensitive data that could potentially be stored online and vulnerable to hacking or accidental disclosure.

Given these concerns, it becomes apparent that feeding data and prompts into publicly hosted LLMs is impractical for most enterprises, particularly those operating in regulated spaces. To mitigate risks while extracting value from LLMs, a more viable approach is to bring the LLM to the data, rather than sending the data to the LLM.

The most effective model for enterprises to balance innovation and data security involves hosting and deploying LLMs within their existing security boundaries. Many large businesses already have robust security measures and governance frameworks in place to safeguard their data. By leveraging this protected environment, data teams can further develop and customize LLMs, allowing employees to interact with them while maintaining a secure and governed ecosystem.

To implement a strong AI strategy, it is crucial to establish a robust data strategy from the outset. This entails eliminating data silos and implementing consistent policies that facilitate teams’ access to the data they require while upholding security and governance standards. The ultimate objective is to have actionable, trustworthy data readily accessible within a secure and governed environment for seamless integration with an LLM.

The Pitfalls of LLMs Trained on the Entire Web

While LLMs trained on the entire web offer vast amounts of data, they present their own set of challenges beyond privacy concerns. These models are susceptible to “hallucinations,” inaccuracies, and the potential to reproduce biases or generate offensive responses. Such shortcomings can introduce significant risks for businesses.

Moreover, foundational LLMs lack exposure to an organization’s internal systems and data, rendering them incapable of providing answers specific to the company, its customers, and even its industry. Consequently, a more effective approach involves extending and customizing existing models to impart domain-specific knowledge.

Extending and Customizing Models

Amidst the prevailing focus on hosted models like ChatGPT, there exists a rapidly expanding array of LLMs that enterprises can download, customize, and deploy internally. Open-source models such as StarCoder from Hugging Face and StableLM from Stability AI offer organizations the opportunity to tailor models to their exact requirements.

While training a foundational model utilizing data from the entire web necessitates extensive amounts of data and computational power, fine-tuning a model for specific content domains requires significantly less data. By leveraging internal data that enterprises trust and that yields relevant insights, organizations can customize their LLMs to cater to their unique needs.

Contrary to popular belief, LLMs do not need to be vast to be effective. The principle of “garbage in, garbage out” applies to any AI model, emphasizing the importance of customization using trusted internal data. While employees may not need to consult an LLM for general information like cooking recipes, they may require specific insights related to sales data, customer contracts, or regional performance. By tuning the LLM on their organization’s data within a secure environment, businesses can derive highly relevant and reliable results.

Additionally, optimizing LLMs to cater to specific use cases within the enterprise can significantly reduce resource requirements. Smaller, specialized models targeting specific use cases tend to demand less computational power and occupy smaller memory sizes compared to models designed for general-purpose or diverse enterprise applications. Customizing LLMs for targeted use cases allows businesses to run these models more efficiently and cost-effectively.

The vast majority of the world’s data, approximately 80%, exists in unstructured formats, including emails, images, contracts, and training videos. Extracting insights from this unstructured data necessitates the utilization of technologies like natural language processing (NLP) to convert it into a usable format for data scientists. Implementing NLP enables organizations to build and train multimodal AI models capable of uncovering relationships between different types of data and surfacing valuable insights specific to their business.

Given the rapidly evolving landscape of generative AI, businesses must exercise caution in their approach to this technology. It is crucial to thoroughly evaluate the models and services utilized and partner with reputable vendors that offer explicit guarantees regarding model performance and security.

Nevertheless, companies cannot afford to remain stagnant in the face of AI’s potential to disrupt industries. Striking a delicate balance between risk and reward is essential. By leveraging generative AI models within their existing security perimeters and closely aligning them with their data, businesses are better positioned to capitalize on the opportunities presented by this transformative technology.

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