FinTechCryptoTask Prompt

Task Prompt to Bind Payment and AI Agent Approvals to the Exact Action They Authorize

This Arc task prompt retrofits your consequential approval flows (wires, payout changes, data releases, and AI agent actions) with dynamic-linking discipline, so every approval is specific to its action and voided by any change. Arc, the deepidv credential gateway, defines an action canon per flow, the structured description the execution layer enforces, with amounts as values, payees and recipients as identifiers, and permissions as scopes, which approval displays must derive from rather than render in parallel. It specifies the binding implementation that ties each approval to the full action tuple, voids it on any post-approval mutation, and sets expiry clocks per action class, plus presence tiering that reserves a liveness-anchored present-person confirmation for irreversible actions, external movement of money or data, and agent authority expansion under the HAPS consequence triggers. A mutation test suite alters destination, amount, permissions, file, and recipient after valid approvals, replays approvals across actions and services, and presents expired ones, expecting rejection with the failed term logged, and an evidence triple records what was displayed, what was confirmed and by which method, and what executed. Any flow where an approval would survive a changed payee or broadened permission comes back as a critical finding with an owner and date. Built for payments, platform, and security leads whose approvals still authorize nothing in particular.

How to use this prompt

1
Open Arc in the deepidv dashboard and paste the full prompt, or run it in Claude, ChatGPT, or Gemini to draft the binding design before wiring it to live flows.
2
Add an INPUT section below the prompt listing each consequential flow (wires, payout changes, data releases, AI agent actions), how its approval screen is rendered today, which fields the approval is bound to, and its current expiry.
3
Run the prompt and read the action canon first: any flow whose approval display is rendered separately from the structure the execution layer enforces is where a changed payee slips through.
4
Hand the binding implementation and mutation test suite to engineering, route the critical findings (approvals that survive a changed payee or broadened permission) to their owners with dates, and give compliance the evidence triple schema.
5
Run the mutation test suite on every release that touches an approval flow, and re-run the full prompt whenever you add a flow or an agent gains new authority.

The prompt

Arc, retrofit our consequential approval flows, wires, payout changes, data releases, and AI agent actions, with dynamic-linking discipline: every approval specific to its action, voided by any change.

Produce:
1. Action canon: per flow, the structured action description the execution layer enforces (amounts as values, payees and recipients as identifiers, permissions as scopes), which approval displays must derive from, never parallel-render.
2. Binding implementation: approvals cryptographically or procedurally bound to the full action tuple, with post-approval mutation of any bound field voiding the authorization and restarting the ceremony, and expiry clocks matched to each action class.
3. Presence tiering: which action classes require, atop the bind, a liveness-anchored present-person confirmation (irreversible actions, external movement of money or data, agent authority expansion), per the HAPS consequence triggers, keeping prompts rare enough to stay meaningful.
4. Mutation test suite: automated drills that alter destination, amount, permissions, file, and recipient after valid approvals, replay approvals across actions and services, and present expired approvals, expecting rejection with the failed term logged each time.
5. Evidence triple: per decision, the record of what was displayed, what was confirmed (and by which verification method), and what executed, exportable for disputes, insurers, and examiners.

Flag any flow where an approval would currently survive a changed payee or broadened permission as a critical finding with owner and date.

Test it in Claude or another LLM

This prompt is built for Arc inside deepidv, where Arc binds live approvals to the actions they authorize. You can dry-run the binding design in any general LLM first with a synthetic flow inventory before wiring it to real approval paths.

1
Paste the full prompt into Claude, ChatGPT, or Gemini, replacing the opening 'Arc,' with a role instruction such as 'Act as a payments security architect retrofitting approval flows with dynamic linking.' Keep the five numbered outputs as written.
2
Under the INPUT section, paste the synthetic sample block below so the model has flows, approval displays, and bindings to retrofit.
3
Add one framing line: 'This is synthetic test data. Treat any approval not explicitly bound to a field as surviving a change to that field, and flag it.'
4
Check the output shape: an action canon, a binding implementation, presence tiering, a mutation test suite, and an evidence triple, with every approval that survives a changed payee or broadened permission listed as a critical finding. If a binding is assumed that the input does not describe, tighten the framing and re-run.
5
Once the shape is right, run it live in the deepidv dashboard where Arc binds real approvals to the actions they authorize.

Synthetic sample data to paste alongside the prompt

Fake test data, safe to share with any LLM. Swap in your own once the output looks right.

FLOWS (synthetic, fake):
- outbound wires: approval screen rendered from the request email, approval bound to amount only, no expiry
- payout bank-detail changes: single click-through confirmation, not bound to the new account number
- customer data exports: manager approval bound to the file name, recipient editable after approval
- AI procurement agent: standing approval to buy from listed vendors, can widen its own permissions with an admin token
PRESENCE (fake): no liveness-anchored confirmation on any flow
EVIDENCE (fake): approvals logged as approver plus timestamp only, no record of what was displayed

Pairs with on deepidv

Sources & further reading

FAQ

What is dynamic linking?

Dynamic linking is the rule, written into European law through PSD2's strong customer authentication standards, that an authorization is specific to the exact amount and payee it approves, so changing either voids it. It ended a family of attacks in which a user approved one transaction and the system executed another, and the same principle now applies to wires, data releases, and AI agent actions, where software sits between what a human confirms and what executes.

What does the Action-Bound Approval Wire-Up produce?

An action canon per flow, a binding implementation with mutation-voiding and expiry clocks, presence tiering for the action classes that need a liveness-anchored present-person confirmation, an automated mutation test suite, and an evidence triple recording what was displayed, confirmed, and executed. Any flow where an approval would survive a changed payee or broadened permission is flagged as a critical finding with an owner and date.

Why must the approval screen derive from the executed action?

If the display is rendered in parallel from different data than the execution layer enforces, the two can disagree, and the human approves one thing while another executes. Deriving the display from the same structured action, then voiding the approval on any change to a bound field, is what makes the confirmation mean something. The mutation test suite proves it by altering destination, amount, and permissions after approval and expecting rejection every time.

Run it with live verification data

These prompts work in any LLM. Inside the deepidv dashboard, Luna, Arbiter, and Arc run them against your real sessions, screening lists, and audit trails.

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