What evidence do enterprise buyers expect for prompt-injection testing?
The entry points, inputs and reproduction counts a reviewer looks for — and why a scanner “hit” isn’t the same as proof.
Coming soonPractical notes on AI security evidence, buyer review, false positives, remediation proof, and enterprise questionnaire support — written for teams whose AI product is going through enterprise security review.
New notes are added as reviews surface recurring questions. Each one maps to a concern enterprise buyers actually raise.
The entry points, inputs and reproduction counts a reviewer looks for — and why a scanner “hit” isn’t the same as proof.
Coming soonThe confident one-liners that pass the first read and collapse the moment a reviewer asks to see what happened.
Coming soonSeverity and confidence are different axes. How to triage scanner noise before it reaches a buyer.
Coming soonThe structure a security team can actually follow: question, test, observed behaviour, judgement, remediation, limits.
Coming soonWhat a screenshot can and can’t support — and what to attach instead so the answer holds under scrutiny.
Coming soonSeparating what failed, what changed, what was re-tested, and what you’re actually calling resolved.
Coming soonWhether retrieved or uploaded content can override instructions or pull restricted data into context.
Coming soonThe full chain — input, user, model output, tool call, authorisation, execution — not the architecture diagram.
Coming soonThe notes are the free part. If a buyer is waiting on AI-specific evidence, bring what you already have and we’ll review what holds.