Managing Privacy Risks in Large Language Models (LLMs)

Although several, some of the common privacy risks associated with LLMs include sensitive data memorization during training, dataset biases and inaccuracies, unauthorized access and accidental data leakage in model outputs, and the possibility of user re-identification through model inference.

Privacy regulations are constantly updating to accommodate evolving technological tools used by businesses and individuals. As such, organizations must understand that laws like GDPR and CPRA apply to LLMs and adopt appropriate controls to ensure compliance.

Risk treatment entails establishing methods to manage recognized risks and building practical execution plans. To ensure ethical and compliant processing, context-specific risk treatment for LLM systems necessitates a feasibility analysis that weighs risks and mitigations, weighs benefits against costs and impacts, evaluates effects on the LLM's purpose, takes user expectations into account, and analyzes trade-offs (performance, transparency, fairness). The four most popular criteria for risk treatment are Accept, Transfer, Avoid, and Mitigate.