SAFEGUARDING SENSITIVE DATA IN THE AGE OF LARGE LANGUAGE MODELS
Large language models (LLMs) are revolutionising business practices by offering significant enhancements to efficiency. But as organisations integrate these advanced tools into business operations, they must navigate a complex web of risks related to sensitive data, including personally identifiable information (PII), protected health information (PHI), and proprietary and confidential business data. The stakes are high: a single message cannot be retracted and could compromise confidential data and damage an organisation’s reputation. To mitigate these risks, organisations must adopt a multifaceted approach to data governance and security.
LLMs learn by analysing extensive datasets, or training data, enabling them to generate responses based on patterns and information they have encountered. This learning process increases the risk of inadvertent data breaches for enterprises that adopt these models, as LLMs memorise and learn from sensitive information and reproduce it in model outputs in later interactions. Here, firms using LLMs to analyse customer data could expose confidential account details if data handling protocols are not strictly enforced. Additionally, if a model is trained on a dataset containing confidential research and development information, it might generate outputs that reveal insights about upcoming products. This risk of exposing sensitive customer or company information is compounded by the difficulty of monitoring LLM interactions with confidential information.
Oct-Dec 2024 Issue
Good Harbor Security Risk Management LLC