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Authority That Matches Use

Access is granted in a hurry and reviewed once a year, if ever. The gap between what an identity holds and what it has ever needed is where most standing risk lives.

Watch it run — sign-up to result

Stage 1 of 5
aribot · Innovation Preview2 of 3 areas
AreasToday vs nextWhere to see itUnder NDA

Each area is described by what changes for you. How it is done is covered under NDA.

Verified outcomes
observable outcome

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

Authority that matches use
observable outcome

What tools do today

Access is recorded at the moment it is granted, and never compared to behaviour.

What you get instead

What an identity holds is shown beside what it has ever actually used.

Concrete example

ci-deploy-bot: 12 scopes granted · 4 ever used → 8 reducible

Already visible in the shipped product: /demos/agent-assurance

Useryou do this step

Point us at your identities

GOES IN

Where access is held

COMES OUT

working…

ILLUSTRATIVE SEED DATA — NOT CUSTOMER DATA

8 agents, 41 service accounts

Run log
t+0.0s [User] Point us at your identities
working on: Where access is held

The problem

Machine identities are granted broad access to get a task moving, and the grant outlives the task. Nothing revokes it because nothing can say whether it is still needed.
Access reviews ask humans to approve permissions they cannot evaluate. Without evidence of use, the safe answer is always to keep it, so nothing is ever removed.
The result is a large, quiet standing entitlement across service accounts and agents — the part of the estate least covered by joiner-mover-leaver processes.

What Aribot does

  • What an identity holds is shown beside what it has actually exercised, so a reviewer has evidence rather than a guess.
  • Unused authority is surfaced as reducible, with the specific scopes named.
  • Proposed reductions can be rehearsed against recent activity before enforcement, so you can see what would have broken.
  • Reviews become continuous rather than annual, because the evidence is always current.

In the product

  1. 1
    Point us at your identities
    In: Where access is held. Out: Inventory.
  2. 2
    See held vs used
    In: Access + activity. Out: Reducible list.
  3. 3
    Rehearse a reduction
    In: Proposed limits. Out: What it would break.
  4. 4
    Apply it
    In: Approved limits. Out: Narrower access.
  5. 5
    Evidence the review
    In: Date range. Out: Reviewable record.
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Why teams use this

Use cases and case studies

Use case

Cut machine access down to what is used

Aribot shows what each agent or machine identity holds beside what it has used, so access reviews rest on evidence and unused access can be removed.

Read the use case One-pager Carousel

Case study · AI governance · Customer zero

Seventeen agent requests refused, each with a reason on record

23
agent identities registered with the gateway
17 of 40
agent requests refused, each with a recorded reason

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