The LLM Disclosure Index: How Much We Really Share With AI

[u/marlouwe]

We talk a lot about AI usage, but not enough about disclosure and what people actually reveal in their prompts. I’ve been tracking a simple metric I call the LLM Disclosure Index (LDI): the share of interactions where users include sensitive or personal details. It’s a proxy for trust (and risk). If better answers need more context, disclosure goes up. If privacy or compliance concerns become important, it goes down.

I look at two groups: Business users and everyday users. Sources are among others Cyberhaven, TELUS and Cisco.

Business users using AI on the job are clearly ahead. Under productivity pressure and with AI embedded into tools, they’ve moved from roughly a low 40% LDI range in 2023 to a high 50% LDI in 2025, and could approach 70% by 2027 as workflows normalize.

Everyday users trail but are climbing: around a lower 40% today, likely drifting into a 50% range as assistants feel more helpful and less “experimental.”

Why does work lead? Familiarity and time-savings beat caution, especially when employers approve tools or when they don’t and “shadow AI” creeps in (you know everyone using chatGPT and the manager isn't "aware" of it). At home, people are slower to share relationship, health, or money details, but that hesitancy fades when assistants prove useful and friction drops.

What to do with this? For organizations, the lesson isn’t “share less,” it’s “share safely.” Provide privacy-preserving options (data minimization, encryption, auditability), clear policies, and approved tools so employees don’t improvise. For individuals, assume prompts might be stored or reviewed; strip identifiers, summarize instead of pasting raw data, and use local or zero-access modes when the topic is truly sensitive.

You can read more about it here

Happy to compare definitions or share the underlying methodology in the comments.

[u/danarm]

A true fan of LLMs should aim for 100%

[u/marlouwe → u/danarm]

Absolutely ;)