Since the inception of ChatGPT, a major concern in the business world regarding conversational AI has been the potential leakage of valuable intellectual property and sensitive data. Some companies went as far as prohibiting employees from utilizing AI tools like GenAI following reports of confidential documents being uploaded, potentially leading to unauthorized access through clever exploitation of security vulnerabilities.
This situation has resulted in a scarcity of vendors in this domain who do not address the issue of private AI in some form. Recently, Dell has joined the conversation, alongside other prominent players such as Box, NetApp, VMware, Google Cloud, IBM, and Red Hat, all offering insights and solutions in this space.
The core concept is relatively simple: organizations can customize and train their own secure generative AI models using proprietary data, ensuring that this AI remains isolated from and does not share data with external AI services. This personalized AI can be deployed on-premises, which has piqued the interest of server and storage companies. Alternatively, it can operate within a public cloud environment, maintaining privacy akin to any other business application hosted in the cloud.
AI-as-a-Service
Furthermore, there is the option for a hybrid approach where SaaS providers like Box, Dropbox, or Salesforce oversee the management of the AI system. By incorporating your company’s data into the mix for contextual insights and training, these SaaS providers leverage their robust security measures, trust frameworks, and data governance protocols to ensure exclusive access and control over your data and its derivatives.
The strategic decision of where to run your personalized AI is heavily influenced by the location of your data and the level of trust you have in the hosting entity. As data is often considered a valuable asset, the effort required to relocate it should be factored in when deciding whether to keep the AI on-premises or in the cloud.
Nevertheless, there is a growing confidence that public cloud providers or SaaS platforms can securely host private AI solutions, especially if you already entrust them with your data and are satisfied with their security measures.
Mitigating the Risks of Data Exposure
While advancements have been made in ensuring the privacy and security of AI systems, challenges persist. For instance, OpenAI has assured users that ChatGPT will not utilize their interactions to enhance its models for those with paid Enterprise subscriptions.
However, achieving a higher level of data protection presents complexities. Safeguarding information from unauthorized disclosure outside the organization is crucial, as not all employees should have unrestricted access. To address this, AI-driven services like Box AI, Dropbox Dash, and Microsoft 365 Navigator are designed with granular permission controls to monitor and regulate data access within the organization.
