Generative AI has the potential to transform, but it requires a lot of data to do so — some of which is proprietary. That creates security and privacy concerns.
Protecting that data is just one advantage of an in-house deployment of generative AI. Read this blog for four more.
What are the security benefits of in-house AI deployments?
In-house deployments of AI allow organizations to maintain complete control over security and data access. By keeping AI models and data within a secure environment, companies can prevent data leaks that may occur with public AI tools. This setup ensures that only authorized personnel can access sensitive information, significantly reducing the risk of data breaches.
How can organizations reduce costs with in-house AI?
Running AI models in-house can lead to significant cost savings. Organizations can tailor their AI solutions to specific needs, avoiding the high expenses associated with public cloud services. This approach allows for better control over operational and capital expenditures, as well as minimizing costs related to data transfer and storage fees.
What advantages does real-time data processing offer?
Processing real-time data at the edge allows organizations to derive insights quickly from valuable data sources, such as sensors and points of sale. This method reduces latency and enhances the efficiency of AI pipelines, making it easier to act on insights without the need to transfer large volumes of data to a central location. This capability is particularly useful for applications like inventory analysis and predictive maintenance.
Sign in with LinkedIn
