The One Big Bucket Problem: The Hidden Cybersecurity Risk in Generative AI
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I've spent more than 20 years working as a cybersecurity professional. My entire job has been to secure and protect your data. So I need to say this as clearly as possible: generative AI systems like ChatGPT and ClaudeAI are not built with the same safeguards we've relied on for decades.
People are sharing financial, health, and personal details with these tools and this breaks the very protections cybersecurity spent 40 years building.
When you share sensitive information with generative AI, it all gets mixed together.
It sits in what I call One Big Bucket (OBB).
There's no partitioning, no segmentation, no isolated containers. Everything blends together, which makes traditional security controls useless. There's no way to enforce HIPAA-style protections or prevent data types from contaminating each other.
Why This Is Bad
In normal IT systems, sensitive data is separated on purpose:
- One database stores names, SSNs, addresses
- Another stores medical records
- Another stores financial information
Each one has different access controls, different encryption, and different audit logs.
If one gets breached, the attacker only gets one category of data, not your entire life.
But with LLMs?
Everything you share becomes part of the same training pool.
Your medical fears.
Your financial stress.
Your relationship problems.
Your kids' names.
Your location history.
Your personality traits and writing style.
It's a complete psychological and behavioral profile, fused together in one place.
If someone ever gained unauthorized access to that system, they wouldn't get one slice of your life, they'd get all of it.
This Breaks Separation of Duties
In federal systems, separation of duties is what keeps your data safe.
We can grant someone access to HR data while restricting access to medical or financial records because the data lives in separate systems.
LLMs destroy this model.
You can't slice access.
You can't restrict certain users to certain buckets.
There are no buckets.
Everything is blended.
Everything is exposed to the same surface area.
Everything violates the security principles we rely on.
The Cybersecurity Nightmare
This is the biggest red flag I've seen in my career.
People are feeding deeply personal information into LLMs—mixing their most sensitive data together—and the AI is training on it. Meanwhile, neither ChatGPT nor ClaudeAI explicitly prohibits you from sharing health, financial, or intimate details.
There's:
- No selective deletion
- No selective access control
- No compliance boundaries
- No audit trail
- No containment
Just one big bucket.
As security professionals, we would never architect a system this way.
How to Protect Yourself
The simplest defense: don't let these companies train on your data.
They say it helps strengthen safety systems and prevent abuse. That may be true, but there's another side of the sword: it creates an entirely new category of risk that nobody can measure yet.
You should contact your elected representatives and push for protections:
- Your Private Intimate Data (PID) should never be used to train Large Language Models.
- Your PID should be encrypted at rest and handled like other high-risk data categories.
Final Thoughts
As a human, I love ChatGPT and ClaudeAI. They both make me faster and more capable.
But as a security professional, the alarm bell is too loud to ignore.
We've never allowed this kind of data mixing before—and for good reason.
One Big Bucket is a disaster waiting to happen.