Private AI is an approach where an organization defines data boundaries, users, models and deployment. Privacy results from architecture and operations, not the product name alone.
Putting it into practice
Start with internal knowledge tasks such as policy questions or contract search. Separate public and confidential data, assign owners and choose the environment that meets requirements. Test revocation and deletion before scaling.
Before you begin
- Permitted data
- Approval owner
- Model data destination
Capabilities and deployment require project-level confirmation. No unsupported certification, ROI or customer claims are made.
Key takeaways
- Select permitted documents
- Assign data owners
- Prepare reference questions and answers
- Test denied access and deletion
- Measure quality, speed and cost before scaling
This is RAGBOX planning guidance, not an external certification standard. Each layer needs an owner, evidence and tests.
Frequently asked questions
Does data have to leave the network?
It depends on deployment and connected services. Map model, OCR, embedding, backup and log traffic before confirming the boundary.
Can an AI answer be trusted immediately?
Check the original, completeness and version, especially for legal, accounting and consequential decisions. Citations support review but do not guarantee accuracy.
How should a project begin?
Choose one defined use case, approved documents, owners and acceptance criteria. Test answer quality, permissions and cost with a small group before scaling.
References
Public sources explain principles; they do not certify or endorse RAGBOX.
- NIST AI Risk Management Framework
- Microsoft: Retrieval-augmented generation
- OWASP: RAG Security Cheat Sheet
- OWASP: Prompt Injection Prevention
Expert review is required before operational use. This is not case-specific legal or tax advice.