The Enterprise Guide to Generative AI Security
Generative AI is transforming industries, but it's also introducing a new attack surface. "Shadow AI"—employees pasting sensitive data into public chatbots—is just the tip of the iceberg. For enterprises building their own copilots and agents, the risks are far more complex.
The Top 3 Risks in 2026
- Prompt Injection: Attackers manipulating the LLM to ignore instructions and reveal system prompts or sensitive data.
- Data Leakage: Sensitive RAG (Retrieval-Augmented Generation) documents being surfaced to users who shouldn't have access permissions.
- Model Hallucinations: In regulated industries like healthcare and finance, a confident wrong answer is a liability.
Securing the GenAI Stack
At Varcio, we implement a "Defense in Depth" strategy for our clients' GenAI applications:
1. Input/Output Guardrails
We use tools like NeMo Guardrails to sanitize inputs before they reach the LLM. If a user tries to jailbreak the model, the guardrail intercepts the request. Similarly, output filters scan for PII (Personally Identifiable Information) before the response is sent back to the user.
2. Private VPC Deployment
Never send sensitive data to public API endpoints without a BAA. We deploy open-source models (like Llama 3 or Mistral) inside the client's private VPC using AWS SageMaker or Azure AI Studio. This ensures data never leaves your controlled environment.
3. RAG with ACL Awareness
When building RAG systems, the vector database must respect Access Control Lists (ACLs). If a junior analyst asks "What is the CEO's salary?", the retrieval system should only search documents they are authorized to view.
Conclusion
Security cannot be an afterthought. By embedding these controls into the design phase, we enable enterprises to innovate with confidence.
