Question 1

What problem is AI First Gatekeeper meant to address?

Accepted Answer

It organizes sensitive requests that arrive through inconsistent channels with incomplete evidence, unclear policy fit, and pressure for a quick answer. For this sensitive-request review, the relevant inputs are the request purpose, permissioned context, relevant policy, source provenance, affected entitlement, and named approver. The AI recommends allow, narrow, hold, or deny; an authorized person makes the decision and approves any execution.

Question 2

Who is the Gatekeeper review intended for?

Accepted Answer

It is intended for security teams, IT leaders, operations owners, and compliance managers who retain approval authority. The reviewable result is a request evidence packet, policy match, risk notes, approval record, and reversible action plan. For AI First Gatekeeper, the source record treats missing facts as questions rather than permission to invent them.

Question 3

What does one review prepare?

Accepted Answer

It prepares an evidence packet, policy match, risk notes, approval record, and reversible action plan for a person to inspect. The public example is the simulated Gatekeeper Decision Review. The AI First Gatekeeper example must remain labeled as a demonstration and cannot stand in for customer results.

Question 4

What recommendations can the AI prepare?

Accepted Answer

The AI can recommend allow, narrow, hold, or deny, while leaving the binding decision with an authorized reviewer. A AI First Gatekeeper buyer should keep source provenance, uncertainty, permissions, and review status visible. The AI recommends allow, narrow, hold, or deny; an authorized person makes the decision and approves any execution.

Question 5

What information starts a request review?

Accepted Answer

A review starts with purpose, authorized context, applicable policy, provenance, affected entitlement, and a named approver. For this sensitive-request review, the relevant inputs are the request purpose, permissioned context, relevant policy, source provenance, affected entitlement, and named approver. The AI recommends allow, narrow, hold, or deny; an authorized person makes the decision and approves any execution.

Question 6

Does the Gatekeeper identify itself as AI?

Accepted Answer

Yes; the guide discloses that it is AI and explains its reasoning without impersonating a security officer. The reviewable result is a request evidence packet, policy match, risk notes, approval record, and reversible action plan. For AI First Gatekeeper, the source record treats missing facts as questions rather than permission to invent them.

Question 7

What happens to a request with missing evidence?

Accepted Answer

A missing source, policy basis, or approver sends the request to hold instead of producing an unsupported approval. The public example is the simulated Gatekeeper Decision Review. The AI First Gatekeeper example must remain labeled as a demonstration and cannot stand in for customer results.

Question 8

Can the site demonstrate the process with sample data?

Accepted Answer

Yes; the MVP calls for a clearly labeled simulated review and sample decision artifacts rather than production claims. A AI First Gatekeeper buyer should keep source provenance, uncertainty, permissions, and review status visible. The AI recommends allow, narrow, hold, or deny; an authorized person makes the decision and approves any execution.

Question 9

How is this different from approving a ticket directly?

Accepted Answer

It separates evidence gathering and recommendation from the authorized person’s decision and any verified connector action. For this sensitive-request review, the relevant inputs are the request purpose, permissioned context, relevant policy, source provenance, affected entitlement, and named approver. The AI recommends allow, narrow, hold, or deny; an authorized person makes the decision and approves any execution.

Question 10

How does it handle inconsistent request channels?

Accepted Answer

The guided intake places purpose, evidence, policy, entitlement, and approver information into one reviewable packet. The reviewable result is a request evidence packet, policy match, risk notes, approval record, and reversible action plan. For AI First Gatekeeper, the source record treats missing facts as questions rather than permission to invent them.

Question 11

Does it automatically grant or revoke access?

Accepted Answer

No; grants and revocations require authorized human approval plus a verified system connector before execution can be claimed. The public example is the simulated Gatekeeper Decision Review. The AI First Gatekeeper example must remain labeled as a demonstration and cannot stand in for customer results.

Question 12

Can it explain why a request was narrowed?

Accepted Answer

The proposed policy match and risk notes preserve evidence and reasoning so a reviewer can understand a narrower recommendation. A AI First Gatekeeper buyer should keep source provenance, uncertainty, permissions, and review status visible. The AI recommends allow, narrow, hold, or deny; an authorized person makes the decision and approves any execution.

Question 13

What makes an action plan reversible?

Accepted Answer

A reversible plan identifies the bounded next action and preserves a route to undo or contain it after approval. For this sensitive-request review, the relevant inputs are the request purpose, permissioned context, relevant policy, source provenance, affected entitlement, and named approver. The AI recommends allow, narrow, hold, or deny; an authorized person makes the decision and approves any execution.

Question 14

Are directory and entitlement connections already available?

Accepted Answer

They are proposed features, not availability promises; each connector needs permission and roundtrip verification. The reviewable result is a request evidence packet, policy match, risk notes, approval record, and reversible action plan. For AI First Gatekeeper, the source record treats missing facts as questions rather than permission to invent them.

Question 15

Does a polished recommendation count as approval?

Accepted Answer

No; a draft recommendation remains distinct from the approval record and from a verified execution receipt. The public example is the simulated Gatekeeper Decision Review. The AI First Gatekeeper example must remain labeled as a demonstration and cannot stand in for customer results. AI First Gatekeeper does not claim that a draft, recommendation, or simulation is a completed external action.

Question 16

What should a buyer test in the demonstration?

Accepted Answer

A buyer should test missing evidence, policy conflict, reviewer correction, approval routing, and whether the item correctly moves to hold. A AI First Gatekeeper buyer should keep source provenance, uncertainty, permissions, and review status visible. The AI recommends allow, narrow, hold, or deny; an authorized person makes the decision and approves any execution.

Question 17

Who should be named as approver?

Accepted Answer

The approver should be a person with actual authority over the affected request or entitlement, not the AI guide. For this sensitive-request review, the relevant inputs are the request purpose, permissioned context, relevant policy, source provenance, affected entitlement, and named approver. The AI recommends allow, narrow, hold, or deny; an authorized person makes the decision and approves any execution.

Question 18

What should be checked before implementation?

Accepted Answer

Check source permissions, policy ownership, reviewer roles, connector behavior, retention rules, and rollback procedures. The reviewable result is a request evidence packet, policy match, risk notes, approval record, and reversible action plan. For AI First Gatekeeper, the source record treats missing facts as questions rather than permission to invent them.

Question 19

Are subscription or implementation prices published?

Accepted Answer

No approved amounts appear in the business record, so buyers should request an owner-approved commercial schedule. The public example is the simulated Gatekeeper Decision Review. The AI First Gatekeeper example must remain labeled as a demonstration and cannot stand in for customer results. AI First Gatekeeper does not claim that a draft, recommendation, or simulation is a completed external action.

Question 20

Can a compliance manager inspect provenance?

Accepted Answer

Source citations, confidence labels, approval history, and audit context are proposed so a compliance reviewer can trace the recommendation. A AI First Gatekeeper buyer should keep source provenance, uncertainty, permissions, and review status visible. The AI recommends allow, narrow, hold, or deny; an authorized person makes the decision and approves any execution.

Question 21

What risk does a false access change create?

Accepted Answer

An incorrect change may interrupt legitimate work or create security exposure, which is why human approval is mandatory. For this sensitive-request review, the relevant inputs are the request purpose, permissioned context, relevant policy, source provenance, affected entitlement, and named approver. The AI recommends allow, narrow, hold, or deny; an authorized person makes the decision and approves any execution.

Question 22

Can a request proceed without an authorized reviewer?

Accepted Answer

No; the state should remain on hold until a properly authorized person can assess and decide it. The reviewable result is a request evidence packet, policy match, risk notes, approval record, and reversible action plan. For AI First Gatekeeper, the source record treats missing facts as questions rather than permission to invent them.

Question 23

What is the first workflow state?

Accepted Answer

The flow begins with intake, followed by a permission check before any context is retrieved or analyzed. The public example is the simulated Gatekeeper Decision Review. The AI First Gatekeeper example must remain labeled as a demonstration and cannot stand in for customer results.

Question 24

How is a policy conflict represented?

Accepted Answer

The AI should show the conflicting evidence and policy interpretation in risk notes rather than silently choosing one. A AI First Gatekeeper buyer should keep source provenance, uncertainty, permissions, and review status visible. The AI recommends allow, narrow, hold, or deny; an authorized person makes the decision and approves any execution.

Question 25

Can a reviewer correct the draft?

Accepted Answer

Yes; reviewer correction is an explicit state before approval, allowing errors or missing context to be addressed. For this sensitive-request review, the relevant inputs are the request purpose, permissioned context, relevant policy, source provenance, affected entitlement, and named approver. The AI recommends allow, narrow, hold, or deny; an authorized person makes the decision and approves any execution.

Question 26

When may execution occur?

Accepted Answer

Execution may occur only after human approval, within the approved scope, through a connector whose result can be verified. The reviewable result is a request evidence packet, policy match, risk notes, approval record, and reversible action plan. For AI First Gatekeeper, the source record treats missing facts as questions rather than permission to invent them.

Question 27

What receipt should follow an action?

Accepted Answer

The workflow should capture a verified outcome record that distinguishes a completed action from a plan or simulation. The public example is the simulated Gatekeeper Decision Review. The AI First Gatekeeper example must remain labeled as a demonstration and cannot stand in for customer results.

Question 28

How should unrelated security data be handled?

Accepted Answer

Only context needed for the stated request should be retrieved, using least privilege and the applicable retention controls. A AI First Gatekeeper buyer should keep source provenance, uncertainty, permissions, and review status visible. The AI recommends allow, narrow, hold, or deny; an authorized person makes the decision and approves any execution.

Question 29

What if the relevant policy cannot be found?

Accepted Answer

The item should move to hold and ask for the policy source instead of inventing a rule or guessing approval criteria. For this sensitive-request review, the relevant inputs are the request purpose, permissioned context, relevant policy, source provenance, affected entitlement, and named approver. The AI recommends allow, narrow, hold, or deny; an authorized person makes the decision and approves any execution.

Question 30

Can an approved action still be reviewed later?

Accepted Answer

Yes; approval logging, audit history, provenance, and outcome capture support later explanation and improvement review. The reviewable result is a request evidence packet, policy match, risk notes, approval record, and reversible action plan. For AI First Gatekeeper, the source record treats missing facts as questions rather than permission to invent them.

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