As more businesses consider integrating AI systems into their operations, there are growing concerns about risks to data privacy as sensitive business information is shared to these artificial intelligence tools.
Common AI tools like ChatGPT require users to upload or share files in order for the large language model to perform the desired task, such as generating financial reports. When this data is uploaded to the cloud, the user cannot be sure how that data will be used or who else can access it. This raises serious data privacy and security questions.
This is where private AI models come in. Platforms like LocalAI, DocMind AI and Ollama allow businesses to sidestep public AI networks by having to use the API from OpenAI.
These platforms are open-source and support different model architectures, such as Diffusers, GGUF and Transformers. The technical requirements for one to use these platforms are often minimal, so a business may not need to hire expert help to install them if an in-house tech team exists.
The platforms can support a variety of functions, such as generating images, producing audio content and running LLMs. Another key advantage of using these platforms is that you don’t need to acquire specialized hardware as the system can run on the hardware that you have available within your premises. However, performance could be enhanced or compromised based on how high the specifications of your consumer-grade hardware are.
You also need to assess what your specific needs are before you select a platform to install for your business. For example, DocMind AI is best suited for businesses that need to mine data available in different formats, summarize or analyze documents. Your specific needs should therefore guide you in selecting the most suitable platform that will perform the tasks you have in mind.
While these platforms that allow businesses to keep AI in-house enhance data privacy by avoiding having to upload sensitive or proprietary information to cloud-based AI systems, it is still important to implement good data privacy measures. For example, it is important to enforce access control measures so that unauthorized individuals don’t easily gain access into your system. It helps when the information that employees can access is regulated according to their level of seniority so that not everyone can access everything on the system.
As individuals and business increasingly rely on internet-based systems, concerns about data protection are increasing. Tech giants like Apple Inc. (NASDAQ: AAPL) are focused on addressing this need and have amassed a loyal following of customers who rely on the company’s proven commitment to security and privacy in every product and system that the firm brings to market.
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