Last week, I saw a dev post a Cursor prompt into Slack asking for help. At first glance, it looked harmless just someone debugging a chain. But as I read through it, I realized it included real client data, likely copied straight from an FRD or Jira ticket.

The request itself was simple: “Anyone know how to make this more efficient?” But buried in the prompt were pieces of strategy, live data, and a fallback policy we use in edge cases, none of it redacted or anonymized. That’s when it hit me: how easily something sensitive could leak in a moment that felt totally routine.

I wasn’t mad at the dev. I was struck by how routine the moment felt. That’s what makes it risky: these leaks don’t happen in dramatic breaches, but in quiet, well-meaning asks for help.

We’ve trained ourselves to protect API keys, credentials, and PII. But we haven’t trained ourselves to treat prompts like internal assets.

Why This Deserves Attention

In many AI workflows, prompts take on more than input, they often become a stand-in for business logic. They often reflect policy decisions, tone guidelines, fallback handling, and internal prioritization frameworks.

Recent research backs this up. A 2024 Salesforce survey found that while 73% of professionals believe AI introduces new security risks, nearly 60% don’t know how to use it securely. That gap is particularly dangerous when you consider that prompts, like config files or macros, often embed the very logic that defines how a company acts.

The Old Definition of “Internal Data” Doesn’t Cut It

Traditionally, we defined internal data as things like customer records, API keys, pricing sheets, and infrastructure secrets.

That definition no longer holds.

Here’s a better one for the AI era:

If leaking it would expose how your company makes decisions, it’s internal data.

This includes things like:

• Few-shot examples that model internal decision processes
• Embedded wiki excerpts used in retrieval systems
• Prompt chains that encode fallback or escalation logic
• Guardrails written as natural language policies
• Human feedback that reflects approval criteria

If it shapes how an LLM behaves, and reflects how your company operates: it needs to be treated like proprietary code or sensitive documentation.

Are There Real Incidents?

The number of confirmed public cases is limited, but telling:

• Samsung engineers pasted proprietary code into ChatGPT. The company responded by restricting external LLM usage and later building their own.
• GitHub Copilot surfaced internal code that had been briefly public, scraped, and then served in unrelated contexts.
• Italy fined OpenAI €15M in 2024 for failing to properly obtain user consent in its training data, highlighting that even prompt data can become subject to compliance scrutiny.
• Air Canada’s chatbot was manipulated into providing incorrect refund information, and the company was forced to honor it. A small mistake in public-facing logic cost real money.
• OWASP’s 2025 GenAI Top 10 lists prompt injection as the #1 risk for LLMs. They’ve documented live examples where attackers altered retrieval pipelines or used indirect prompts to extract data.

These aren’t isolated edge cases anymore. As adoption grows, so do the attack surfaces.

What You Might Be Missing

Even if you’re not pasting secrets, your prompts might still reveal:

• Internal error-handling strategy
• How your team approaches sensitive user interactions
• Escalation logic or internal triage policies
• Pricing guidance or approval heuristics
• Embedded rules or rationale from internal support docs

In fact, OWASP and Qualys report over 1.65 million AI-related vulnerability detections, many tied to prompt logic or misused retrieval patterns.

How to Use LLMs Without Leaking Your Strategy

Manage prompt engineering with the same care and discipline you apply to software engineering. Here’s how that looks in practice:

Abstract logic into code.
Create a prompts/ directory in your repo. Version it. Document it. Flag prompts that encode sensitive policy. Treat them like application config—because they are.

Sanitize inputs before injection.
Use preprocessing middleware or prompt-wrappers that automatically scrub inputs for names, IDs, or metadata. Tools like langchain, guardrails.ai, or even simple regex-based filters can be wired in.

Use examples wisely.
Build templates that standardize how examples are constructed. If you’re using real-world cases, anonymize them with dummy values that cannot be reverse-engineered.

Be mindful of logging.
Use logging middleware with redaction layers. Libraries like Pydantic or Structlog can support structured redaction. Log prompt types or metrics, not full text.

Restrict access and deploy discipline.
Assign ownership for sensitive prompt chains. Establish code review workflows before prompts hit production. Maintain audit trails.

⚖️ What About Velocity?

It’s fair to ask: Won’t all of this slow us down?

In the short term. Maybe a little. But the goal isn’t to bottleneck AI usage. It’s to create trustable guardrails so your teams can move faster without constantly looking over their shoulder.

Think of it like CI/CD. At first, it feels like friction. But over time, it unlocks speed with safety.

The real risk isn’t adding a review step. The real risk is leaking strategy in a prompt you didn’t even realize was sensitive.

A Simple Framework for Classifying Prompt Sensitivity

If it would raise eyebrows in a job interview, it shouldn’t be in a raw prompt.

What You Can Do This Week

Want to reduce your risk quickly? Start here:

• Review your Cursor prompt history. Look for anything that reveals sensitive logic, names, or business policy.
• Spin up a shared **prompts/** directory. Treat prompts like config or templates. Give them ownership and version control.
• Add a redaction layer to your logging. Strip or mask anything sensitive before it hits your backend.
• Normalize prompt hygiene. Kick off a short session with your dev team, this is just the new cost of working with AI.

Even one of these steps will make your environment safer.

Final Thought

Internal data used to be what we stored.
Now, it’s what we say, especially when we say it to a model.

Prompts aren’t glue code. They’re interfaces to your organization’s logic, tone, and judgment.
And if you’re not treating them that way, you’re already leaking more than you think.

In an AI-first world, your prompt is your process. Guard it accordingly

✉️ Call to Action

If you’re working on AI tooling, rolling out LLMs across your org, or just starting to think about prompt security, I’d love to hear how you’re handling it.

Drop your thoughts in the comments, or reach out on LinkedIn or X .

Let’s trade notes before the next leak becomes a case study.