Data is a valuable asset that needs to be protected and shouldn’t be sent to large language models, as we saw when Samsung employees accidentally leaked top-secret information on ChatGPT. Organizations have to balance the benefits of using large language models like ChatGPT with the need to keep their data secure. One solution that many organizations are returning to is using machine learning models on-premises. This lets you access the benefits of ChatGPT while keeping all of your sensitive data behind your firewalls.
Here are some of the key benefits of using large language models on-premises:
- Enhanced Security: Organizations can reduce the risk of data breaches and cyber attacks. This is especially important for organizations that handle sensitive information, such as healthcare providers, financial institutions, and government agencies.
- Greater Control: By keeping the data on-premises, organizations have complete control over the data, which means they can dictate who has access to it and how it is used. This can be important for organizations that need to comply with data privacy regulations, such as GDPR and HIPAA.
- Reduced Latency: On-premises machine learning models can also help reduce latency. Because the data is stored locally, it can be processed faster, which can be important for organizations that need real-time insights.
- Cost Savings: Organizations can avoid the costs associated with cloud storage and data transfer by keeping the data on-premises.
Using machine learning models on-premises is a viable solution for organizations that need to keep their data secure and comply with data privacy regulations. By keeping the data on-premises rather than sending it elsewhere, organizations can have greater control over their data, reduce latency, save costs, and scale their infrastructure as needed.
Organizations that handle sensitive information, such as healthcare providers, financial institutions, and government agencies, need to keep their data secure and comply with data privacy regulations. Using machine learning models on-premises can be a viable solution that allows organizations to have greater control over their data, reduce latency, save costs, and scale their infrastructure as needed.
Additionally, Jaxon’s unique Large Language Model + Knowledge Graph offering takes this approach a step further by providing a secure on-premises environment and marrying it with a knowledge graph that offers “guard rails.” By combining these two technologies, organizations can create intelligent applications that revolutionize how they interact with information. With Jaxon, organizations can enjoy increased security and models that are more in tune with their data, ultimately leading to better insights and decision-making.
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