Use case
What Aribot is building next, by buyer problem
A preview of the problems Aribot is working on next, such as proving a fix worked and matching machine access to what is actually used.
Three problems we are solving next. Described by what they cost you today and what changes when they are fixed — implementation detail is covered under NDA.
Each area is described by what changes for you. How it is done is covered under NDA.
What tools do today
A fix is reported done when the instruction was sent.
What you get instead
A finding closes only after the resource has been re-read and agrees.
Concrete example
Bucket re-read after the change: public access blocked → finding closed
Already visible in the shipped product: /demos/ai-remediation
GOES IN
Your current pain
COMES OUT
working…
ILLUSTRATIVE SEED DATA — NOT CUSTOMER DATA
"we cannot prove fixes worked"
When it comes up
Every tool in the shortlist claims automated remediation, and the demos all end at the moment the fix is submitted.
What changes
You can watch a finding stay open until the resource is re-read, on a live demo, before any sales conversation.
When it comes up
Machine identities accumulated access over years and the review has no evidence base, so it keeps being deferred.
What changes
Granted scopes are shown beside the ones ever exercised, so the reducible set is evidenced rather than estimated.
When it comes up
A green score gives no way to tell a tested control from one nobody ever looked at.
What changes
Unmeasured is a visible state on screen and is excluded from the score rather than defaulted into it.
Why teams use this
Use case
A preview of the problems Aribot is working on next, such as proving a fix worked and matching machine access to what is actually used.