The New Security Perimeter
Generative AI introduces a fundamentally new paradigm for enterprise security. Traditional data loss prevention (DLP) tools were built to monitor distinct files and structured strings like social security numbers. But what happens when the requested data is synthesized on the fly from a dozen different sources?
1. Private Infrastructure is Non-Negotiable
If you are passing sensitive customer data or proprietary IP to a public model, you are no longer in control of your data. The first step to securing enterprise AI is ensuring you are using dedicated, private models.
- VPC Deployments: Run models within your own Virtual Private Cloud.
- Zero Data Retention: Ensure any third-party APIs have strict Zero Data Retention (ZDR) agreements in place.
- Self-Hosted Weights: When possible, utilize open-source models (like Llama 3 or Mistral) managed entirely internally.
2. Role-Based Data Hydration
In a typical RAG (Retrieval-Augmented Generation) setup, the AI needs context to answer a question. Security breaks down if the AI retrieves documents that the user asking the question doesn't usually have access to.
The AI's context window must be dynamically hydrated based on the user's explicit IAM (Identity and Access Management) permissions.
Rule of Thumb: If an employee cannot access a file in SharePoint, the AI should not be able to read that file to answer their prompt.
3. Auditing the AI
You need robust logging for every AI interaction. This means logging:
- The exact prompt sent by the user.
- The specific documents the AI retrieved for context.
- The exact output generated.
These logs must be immutable and easily exportable for compliance teams. Orvyn handles this natively with SOC2 Type II compliant infrastructure out of the box.