We all drill the message into our children: “Whatever you put online will stay there forever.”
And yet — when it comes to business, many of us treat internal strategic documents, intellectual property, client data and process secrets as if they were ephemeral, disposable and safe to upload into large‑language models (LLMs) without a second thought.
That’s a dangerous double‑standard.
The paradox
At home we say: don’t post your private stuff, don’t overshare, don’t reveal what you’ll regret later.
In the boardroom or on the engineering floor, we’re quite willing to paste in architecture diagrams, client‑lists, commercial go‑to‑market plans, even customer data — into platforms that may permanently absorb, reuse or expose them.
And yes — this is business information, not selfies. The stakes are higher.
Because once that data goes into an uncontrolled LLM (especially a public one) it may:
become part of training data and be retrievable by someone else’s prompt;
sit in a place you cannot delete, even when you ask;
compromise competitive advantage, violate confidentiality or slip into compliance risk.
force you into a “trace‑it‑back” scenario you cannot unwind.
For example:
In 2024/25 a report found 77% of employees have shared sensitive company data into public Gen AI tools.
Engineers at Samsung Electronics were found to have pasted internal code and strategic documents into the public chatbot environment.
One study found ~8.5% of employee prompts to popular LLMs contained sensitive data (customer, billing, authentication data) — 46% of those leaks were customer data.
Why your business data might “stay forever”
When you upload into a public LLM or an uncontrolled AI tool, you surrender much of the visibility, delete‑control and provenance you would normally have. Even if you think you’re just asking a question, the input (and possibly your entire document) becomes part of model behaviour.
And since a true AI engine must become self‑educating and learns from inputs, any data you share may become embedded in the model’s future responses. Think of it as permanent ink.
Contrast that with what we teach children: once something is on the internet it may never go away.
It’s the same risk, just magnified when we talk business: your source of competitive advantage (processes, client relationships, machine‑learning features, regulatory strategies) may be floating in the public or semi‑public model feed.
The “What” vs “How” distinction
Here’s the practical trade‑off:
It’s safe (and helpful) to share what you do: e.g., “We provide client‑specific regulatory intelligence via an AI/LLM interface to compliance teams.”
It’s not safe to share how you do it: e.g., “We ingest audit‑trail files from our 8 legacy systems, transform them into vector embeddings, fine‑tune a 3‑billion‑parameter model overnight and host it on unmanaged public cloud with shared‑tenant architecture.”
Why? Because the “how” is effectively your secret sauce — the machinery, the architecture, the data flows, the unique IP. Exposing that invites copying, leakage or worse.
At the same time, you want to stay relevant: you must talk about your innovation, your methodology, your capability—but done in a way that does not compromise your property or your clients.
How to resolve the dilemma: Trust‑worthy, controlled LLM usage
As a strategy for privacy‑bound, risk‑averse firms (exactly our audience at Ariadne Thread Solutions), here are guardrails:
Classify your data — Identify what is simply “shareable context” (industry trends, non‑sensitive SOPs, policy frameworks) versus what is sensitive (customer PII, trade secrets, competitive processes).
Control the environment — Use private‑cloud or on‑premise LLM infrastructure where you control data ingress, egress, logging, delete rights and model training.
Segment “what” vs “how” in your messaging — Publicly talk about your outcomes, your results, your value; privately keep your architectures, code, and client‑specific designs locked down.
Govern and monitor usage — Establish AI‑use policies, user‑permissions, logging and DLP for AI tools. Many firms are blindsided by “shadow AI” where employees paste data into public tools outside IT oversight.
Delete and audit — Make sure you retain control: data linked to models must be auditable and deletable. If you cannot delete or retrieve provenance, treat it as high risk.
Final word
Teaching our kids “once it’s online, it’s forever” is wise. We need to apply that same wisdom to our business: once our data enters a large language model in an uncontrolled way, it may be forever too.
At Ariadne Thread Solutions we believe you can use AI/LLMs safely — you just need the right architecture, guardrails, culture and our care )).
Let’s talk about how you can move forward without compromising your competitive advantage or regulatory compliance.
