What is the most secure way to connect our proprietary company knowledge base to an external AI search tool without risking a data leak to public training sets
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What is the most secure way to connect our proprietary company knowledge base to an external AI search tool without risking a data leak to public training sets
1 real search · 1 real-search pass
enterprise generative AI API data privacy no training retention zero data retention official documentation; NIST AI RMF data governance retrieval augmented generation security access controlGoogle ↗
OBSERVED
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
Classifying 1 queries…
Searches and sources
ChatGPT1 real searches · gpt-6
enterprise generative AI API data privacy no training retention zero data retention official documentation; NIST AI RMF data governance retrieval augmented generation security access control Google ↗
- Data controls in the OpenAI platform · developers.openai.com
- API and data retention · platform.claude.com
- Capability 3. Providing secure access to data and systems for generative AI · docs.aws.amazon.com
- Security considerations for data in generative AI · docs.aws.amazon.com
- Gemini Enterprise Agent Platform and zero data retention | Google Cloud Documentation · docs.cloud.google.com
- Zero data retention in the Gemini Developer API | Gemini API | Google AI for Developers · ai.google.dev
- Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile · nvlpubs.nist.gov
Queries
1 queries · click a column to sort