Neil's "Maintaining Enterprise Data Privacy in a Data-Hungry World" has been published on Finextra this week. The article discusses the challenges of maintaining data privacy in the era of large language models (LLMs) and artificial intelligence (AI). As these models require vast amounts of data to function, they pose a significant risk to sensitive information. Neil highlights the following key risks:
- Data Extraction and Exposure : LLMs can memorize and reproduce sensitive information, leading to data breaches.
- Inference Attacks : Attackers can extract sensitive information or infer patterns from model outputs without direct access to data.
- Unintended Biases : Biased training data can result in discriminatory outcomes.
And then looks into how to mitigate these risks, with recommendations for strategies to protect enterprise data.
The article also suggests exploring the use of private Small Language Models (SLMs), which are trained exclusively on-premise with permissioned data. This approach can help organizations maintain control over sensitive information while harnessing the power of AI.
Read Neil's article here