---
id: "open-question-ai-data-privacy"
type: "open-question"
source_timestamps: ["¶49"]
tags: ["artificial-intelligence", "data-privacy", "llm-training"]
related: ["claim-genai-lacks-depth"]
resolutionPath: "Development of widespread, accessible enterprise-grade LLMs that guarantee zero data retention or training on user inputs."
source_url: "https://hbr.org/2025/10/innovating-at-the-core-and-for-the-future"
source_title: "Innovating at the Core—and for the Future"
sources: ["futures"]
sourceVaultSlug: "hbr-seg-futures"
originDay: 2
articleStem: "hbr-cl-91-innovating-core-and-future"
sourceUrl: "https://hbr.org/2025/10/innovating-at-the-core-and-for-the-future"
sourceTitle: "Innovating at the Core—and for the Future"
---
# How can executives use AI without forfeiting IP to public LLMs?

**Open question:** How can high-profile individuals and companies leverage AI without inadvertently training public models on their proprietary insights?

Nooyi expresses frustration that when her staff prompted GenAI using her past public interviews, that data was fed back into the large language model to train it further. She notes she is 'not particularly fond' of them using her data to get better. This surfaces directly from her test described in [[claim-genai-lacks-depth]].

**Likely resolution path:** Development of widespread, accessible enterprise-grade LLMs that guarantee zero data retention or training on user inputs.

**Enrichment.** Emergent enterprise-LLM literature and vendor offerings increasingly market 'no-training-on-customer-data' options, directly addressing the concern Nooyi raises.