

IMAGINE DISCOVERING THAT HALF YOUR ENGINEERING TEAM HAS BEEN UPLOADING YOUR PROPRIETARY CODE TO CHATGPT — NOT TO SABOTAGE THE COMPANY, BUT TO DEBUG FASTER.
This isn’t hypothetical.
It happened to Samsung.
MAY 2023: THREE LEAKS IN THREE WEEKS
Samsung engineers, working on cutting-edge semiconductor technology, faced impossible deadlines.
They discovered ChatGPT could optimize code in seconds.
So they pasted:
→ Proprietary source code for chip fabrication databases
→ Yield measurement algorithms (worth billions in competitive advantage)
→ Internal strategy meeting recordings
They didn’t think they were leaking secrets.
They thought they were using a calculator.
But ChatGPT isn’t stateless.
OpenAI retained those inputs.
Samsung’s IP became training data on servers in the United States.
Samsung responded with an immediate company-wide ban.
Problem solved, right?
WRONG.
THE BAN MADE IT WORSE
Studies show that banning AI doesn’t stop usage — it drives it underground.
The 2024–2025 data is shocking:
• 50% of knowledge workers use unauthorized AI tools (Netskope)
• 46% refuse to stop, even when their company bans it
• 38% admit to sharing sensitive work data with AI platforms
• 72% use personal accounts (completely invisible to IT)
• 485% increase in source code uploaded to AI tools (year-over-year)
Why?
Because employees face a brutal choice:
→ Ship the project on time (using AI)
→ Follow security policy (and fall behind)
When security becomes an obstacle to competence, high performers bypass it.
They use personal phones.
They connect via 4G hotspots.
They tell themselves, “I’ll just change the variable names.”
This is SHADOW AI.
And it’s the biggest data breach you can’t see.
WHY FIREWALLS DON’T WORK
Your corporate firewall blocks chat.openai.com.
Great.
But can it block:
• An employee’s personal smartphone on their desk?
• The 317+ other AI apps they can use instead?
• Browser extensions scraping your internal CRM?
Traditional security assumes the perimeter is protection.
When the threat is the employee’s need to be productive, the perimeter is irrelevant.
THE ONLY SOLUTION THAT WORKS: GIVE THEM A BETTER AI
Veriprajna takes a different approach.
Don’t ban AI.
Provide sovereign AI that’s better than public tools.
We deploy PRIVATE ENTERPRISE LLMs inside your infrastructure:
✅ Llama 3 models running on your own servers
✅ Air-gapped VPC — data physically cannot leave your network
✅ Custom fine-tuning on your documentation and code
✅ Integrated into VS Code, Slack, and internal tools
✅ Faster than public APIs (no internet latency)
✅ NVIDIA NeMo Guardrails automatically redact PII and block unsafe prompts
WHEN ENGINEERS HAVE AN INTERNAL AI THAT:
• Understands your legacy codebase better than ChatGPT
• Responds in 20ms instead of 800ms
• Never sends data outside your walls
• Is officially sanctioned and embedded in their workflow
They stop using ChatGPT.
Not because you forced them.
Because yours is better.
THE ECONOMICS
“But isn’t self-hosting expensive?”
At low usage, maybe.
At enterprise scale, APIs explode in cost.
Example: Mid-sized company processing 1 billion tokens per month
• GPT-4 API: $15,000 per month
• Self-hosted Llama 3 on cloud GPUs: $3,000 per month
That’s 50–70% cost savings.
And you own the infrastructure.
That doesn’t even count avoided costs:
• Data breach fines (GDPR up to 4% of global revenue)
• IP theft (Samsung’s leaked yield algorithms were worth billions)
• Regulatory penalties (ITAR and defense contractors cannot use foreign APIs)
THE SAMSUNG LESSON IS CLEAR:
You can’t ban your way to AI security.
The workforce has tasted productivity gains.
They won’t go back.
Your choice:
→ Let them use uncontrolled Shadow AI and leak everything
→ Give them controlled, sovereign AI and secure everything
Available for consultations on Private Enterprise LLM deployments tailored to your industry and compliance requirements.
📖 Read the full technical whitepaper here:
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Connect:
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