How to minimize data risk for generative AI and LLMs in the enterprise
To strike a balance between risk and reward, bring generative AI LLMs close to your data and within your existing security perimeter.
How can enterprises minimize data risks with generative AI?
To minimize data risks, enterprises should bring generative AI LLMs close to their data and operate within their existing security perimeter. This approach allows businesses to utilize LLMs without exposing sensitive information to publicly hosted models, which can lead to security, privacy, and governance issues.
What are the concerns with publicly hosted LLMs?
Enterprises are concerned that publicly hosted LLMs may 'learn' from their prompts and inadvertently disclose proprietary information. Additionally, there is a risk that sensitive data could be stored online, making it vulnerable to hackers or accidental exposure.
How can enterprises customize LLMs for their needs?
Organizations can customize LLMs by training them on their internal data within a secure environment. This involves eliminating data silos and ensuring access to trustworthy data. By fine-tuning models with relevant internal information, businesses can enhance the accuracy and relevance of the insights generated by the LLMs.
How to minimize data risk for generative AI and LLMs in the enterprise
published by Divergent IT
Divergent IT is a tech service operational consulting & strategy firm. Divergent IT partners with CIOs, business owners, and Non-Profits to develop strategy and implementation across their business including: cybersecurity, remote maintenance management (RMM), IT strategy, on-site maintenance and more.
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