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.
Putting it into practice
Survey tools by starting with employees’ needs. Classify data, publish approved tools and provide a useful alternative plus an approval channel. A ban alone may not address why people choose external tools.
Before you begin
- Current tools
- Data policy
- Approved alternative
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.