How to minimize data risk for generative AI and LLMs in the enterprise | The Tech Robot
While generative AI can enhance productivity and unearth new ideas, it also raises security, privacy, and governance problems. Enterprises are concerned that Large Language Models (LLMs) may learn from their prompts, leak confidential information, and expose critical data to hackers. Most businesses, particularly those in regulated industries, will find it impossible to input data and prompts for publicly hosted LLMs. Companies must carefully assess their approach to extracting benefits from LLMs while reducing these dangers. READ FULL BLOG HERE
Work within your current security and governance boundaries
To strike a compromise between data protection and creativity, organizations should apply the LLM to their data, enabling data teams to modify and customize it within their existing security perimeter. Large enterprises should operate and execute LLMs under their existing security environment, reducing silos and adopting easy data access controls. The objective is to have practical, reliable data that can be accessed quickly with an LLM in a safe, managed environment.
Create domain-specific Large Language Models (LLMs)
LLMs educated on the internet might offer privacy risks, errors, and biases because they have no access to an organization’s systems and data. Enterprises may modify and customize models, including hosted models like ChatGPT and open-source models, to make LLMs more relevant to their businesses. READ MORE
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