Security
Guides for executives, IT and data owners. Understand visible risks, then define your Private AI scope together.
Your AI. On your terms.
This is RAGBOX planning guidance, not an external certification standard. Each layer needs an owner, evidence and tests.
Capabilities and deployment require project-level confirmation. No unsupported certification, ROI or customer claims are made.
AI governance: policy into practice — Security
AI records should identify who did what, with which data and model, when, and who may review it. Balance useful evidence against sensitive information in logs.
Learn moreYour data, your boundary — Security
Data control covers originals, chunks, embeddings, indexes, caches, answers and logs. Know where each lives, who may access it and how to delete or restore it.
Learn moreShadow AI at work — Security
Shadow AI is AI use outside organizational approval or visibility. Risk depends on data, tools, accounts and contracts. It does not mean every public AI service is unsafe.
Learn moreFrequently 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.