ChatGPT quickly gained 100 million users in just three months, making it the fastest-growing new product. This demonstrated the potential and power of large language models (LLMs), prompting other major companies like Google, Facebook, and Anthropic to develop their own models.
Companies that fully embrace AI strategies and technologies are flourishing, while those who lag behind risk getting left behind. LLMs are currently the most powerful AI engines, and forward-thinking enterprises are devising strategies to leverage this groundbreaking tool.
However, concerns about the safety of large language models are valid and frequently raised by potential users. Sharing data is not necessary to inadvertently leak information; even asking ChatGPT a question can reveal internal knowledge about an organization’s future plans. Despite being OpenAI’s largest shareholder, Microsoft advised its employees to avoid using ChatGPT due to security risks.
To utilize LLMs safely and responsibly, organizations can employ private models that operate within their secure internal IT infrastructure without relying on external connections. By containing these models within their own infrastructure, enterprises can protect their knowledge and data.
Implementing private models requires buy-in from all stakeholders in the organization, and a risk assessment should be conducted prior to implementation. Well-defined policies for their usage should be established, similar to other critical IT resources. Access control for key employees, especially those dealing with sensitive information, must be implemented.
Compliance with standards like ITAR, GDPR, and HIPAA should be considered for organizations that are required to adhere to them. For instance, using ChatGPT for case preparations by unaware lawyers would be a clear breach of attorney-client privilege.
With private models, enterprises retain control over the model’s training, ensuring that the training dataset is appropriate and that the resulting model is compliant with standards. As these models handle sensitive data in a secure manner during runtime, they do not retain any information in their short-term memory or context. This division of knowledge between permanent and temporary storage offers great flexibility in designing systems that comply with standards.
Private models have an additional advantage over ChatGPT in that they can learn organization-specific “tribal knowledge” that is often locked away in emails, internal documents, project management systems, and other data sources. This wealth of information, incorporated into private models, enhances their performance within the enterprise.
A divide is emerging between those who have adopted AI successfully and those who have not. However, as with any new technology, it is crucial to assess the risks and rewards across the organization before rushing to a solution. With proper project management and involvement of all stakeholders, enterprises can securely and effectively implement AI through private LLMs, ensuring the responsible deployment of AI agents.
Oliver King-Smith, the CEO of smartR AI, is involved in developing applications based on the evolution of interactions, changes in behavior, and emotion detection.
