09/29/2026
Generative AI Risk Management: Prompt Leakage, Model Memorization, and Output Accountability
Most life sciences AI governance frameworks were written for predictive models and do not address the risks that are specific to generative AI. This article works through five of them with a control set for each: prompt and context leakage, including vendor logging, human review programs, subprocessors, data residency, and what an enterprise agreement actually promises versus a consumer tier; training data memorization and extraction, with a precise account of what the research measured and why the risk profile differs between a hosted model and a model fine-tuned on your own corpus; prompt injection and indirect prompt injection through retrieved documents, the risk most GxP retrieval-augmented generation designs underrate; output accountability, meaning who signs a generated document and what the signature attests to; and provenance and disclosure....
Most life sciences AI governance frameworks were written for predictive models and do not address the risks that are specific to generative AI. This article works through five of them with a contro…