Understanding the risks of public LLMs and how to secure your business data while still leveraging the transformative power of AI.
In the rush to adopt Generative AI, many businesses are inadvertently exposing their most sensitive assets: their data. While tools like ChatGPT and Claude are powerful, using the public versions for corporate work can be a recipe for disaster.
Samsung banned employee use of ChatGPT after engineers accidentally leaked proprietary semiconductor code. Amazon, JPMorgan, and Apple have similar restrictions. The risk is real.
When you paste a contract, a piece of proprietary code, or a customer list into a public chatbot, that data may be used to train future versions of the model. This means your trade secrets could potentially surface in a competitor's query months later.
The risks extend beyond training data:
Public providers may store your queries indefinitely for quality improvement, creating a permanent record of sensitive business information.
GDPR, HIPAA, and SOC2 require strict data control. Public AI services often can't provide adequate compliance guarantees for regulated industries.
Your data passes through multiple servers and jurisdictions, creating exposure points where unauthorized parties could potentially access it.
"Private AI" refers to deploying Large Language Models (LLMs) within your own secure infrastructure, ensuring your data never leaves your control. The model comes to the data, not the other way around.
Run models on your own physical servers within your data center. Maximum control, ideal for highly regulated industries (finance, defense, healthcare). Requires significant infrastructure investment.
Deploy in AWS, Azure, or GCP using dedicated Virtual Private Cloud instances with guaranteed data isolation. Flexible, scalable, and compliant with most regulatory frameworks.
Sensitive operations run on-premise while less critical workloads use private cloud. Balances security with cost efficiency and operational flexibility.
Meet GDPR, HIPAA, SOC2, and industry-specific requirements by keeping PII and sensitive data within your firewall. Full audit trails and access control.
Fine-tune models specifically on your company's jargon, documents, and workflows. Achieve 40-60% better accuracy than generic models for domain-specific tasks.
While setup requires investment, high-volume usage (10K+ queries/day) becomes cheaper than per-token API fees. Predictable costs, no surprise bills.
No rate limiting, guaranteed uptime SLAs, and latency optimized for your infrastructure. Critical for production applications serving thousands of users.
| Scenario | Public API | Private AI |
|---|---|---|
| 10K queries/month | ~$500/mo | ~$1K/mo* |
| 100K queries/month | ~$5K/mo | ~$3K/mo |
| 1M queries/month | ~$50K/mo | ~$8K/mo |
*Includes infrastructure, model hosting, and maintenance. Prices are approximate and vary by model size and configuration.
Moving from public to private AI doesn't have to be disruptive. Here's a proven approach:
For serious enterprises, the question isn't "Should we use AI?" but "How do we use AI safely?" Private AI offers the transformative power of intelligence without the unacceptable risks of data exposure.
It's the only sustainable path forward for industries like law, finance, and healthcare—and increasingly, for any business that values its competitive advantage and customer trust.
Let's discuss how a private deployment can secure your data while unlocking AI's full potential.
Schedule Free Consultation