Real ChatGPT answer · index seed7H79pc3JDz0completeB2B software · AI and automation toolsinformational
What architectural considerations are important when designing a secure pipeline that passes sensitive CRM data into an external language model
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What architectural considerations are important when designing a secure pipeline that passes sensitive CRM data into an external language model
1 real search · 1 real-search pass
NIST AI Risk Management Framework generative AI profile data privacy third party LLM OWASP Top 10 for LLM Applications prompt injection sensitive information disclosureGoogle ↗
OBSERVEDConsequence
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.
ChatGPT real searches (1)
Classes come from the query-classifier (a local Laya model). 1 of 1 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT1 real searches · gpt-6
NIST AI Risk Management Framework generative AI profile data privacy third party LLM OWASP Top 10 for LLM Applications prompt injection sensitive information disclosure Google ↗
- OWASP Top 10 for · genai.owasp.org
- LLM Prompt Injection Prevention · cheatsheetseries.owasp.org
- Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile · nvlpubs.nist.gov
- Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile · nist.gov
- Security risks and mitigations · genai.owasp.org
- AI governance and risk management · nist.gov
- Prompt injection mitigation · cheatsheetseries.owasp.org
Queries
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