How do you identify AI risk vectors before deploying a new AI tool?
Map every point where data enters or leaves the tool: what's typed into it, what it retrieves, what it stores, and what third parties can access. Each of those points is a candidate risk vector, and each should be evaluated for what sensitive data could pass through it.
Related terms
Sensitive Data
Any information that could cause harm, embarrassment, discrimination, or loss if exposed to an unauthorized party — a broader category than regulated data, defined by potential impact rather than by a specific legal framework.
Hallucination (AI Hallucination)
An AI model generating output that's fluent, confident, and entirely wrong — not a bug that occasionally slips through, but a structural property of how these models work, which means the real question isn't whether hallucination happens, but what catches it before someone acts on it.
HIPAA (Health Insurance Portability and Accountability Act)
The US law governing how protected health information can be used, stored, and shared — and one of the clearest examples of a regulation written decades before AI existed that now has to be applied, without modification, to AI tools its drafters never anticipated.
Human-in-the-Loop
The requirement that a person review, approve, or be able to override an AI system's output before it becomes a real decision — the single control most directly responsible for catching hallucinations, biased outcomes, and consequential errors before they reach the person they affect.
See How do you identify AI risk vectors before deploying a new AI tool? in practice
Questa AI anonymizes sensitive data before it reaches any AI model — across documents and live prompts, with governance and data-residency control.