FinTechReview Prompt

AI Prompt for Erie County Biometric Privacy Erasure Audits

This prompt turns Arbiter, the deepidv autonomous red-team agent, into a biometric-erasure auditor for the Erie County Biometric Privacy Act. It traces every place a face template, liveness frame, or biometric hash can persist across your New York consumer endpoints, then proves whether each one purges immediately after session verification or lingers in caches, queues, logs, and backups. It returns a residency map, a per-store erasure verdict, a retention-conflict inventory where biometric purge collides with records-retention duties, and concrete remediation with owning agents. Built for fraud engineering and compliance leads at fintech firms serving Western New York consumers who need evidence of erasure, not an assumption.

How to use this prompt

1
Open the deepidv dashboard and address the prompt to Arbiter, or paste it into Claude, ChatGPT, or Gemini if you want a point-in-time architecture review without live endpoint telemetry.
2
Replace the INPUT section with your NY consumer endpoint inventory, the biometric data stores each one touches (caches, queues, object storage, logs, backups, CDN), your stated post-session erasure policy, and any conflicting records-retention obligations.
3
Run the loop and read the erasure verdict table first: every store is rated purged-on-session, delayed-purge, or persistent, with the evidence or the evidence-absence reason behind each call.
4
Route persistent and delayed-purge findings to the owning engineering teams as blocking fixes, and send any biometric-versus-records-retention conflict to compliance and outside counsel before you change a retention schedule.
5
Re-run the audit after each remediation sprint and on every new NY consumer endpoint, so the residency map converges to immediate post-session erasure and stays there.

The prompt

Arbiter, run an audit loop across our New York consumer traffic endpoints to verify that our architecture satisfies the strict erasure criteria mandated under the Erie County Biometric Privacy Act, confirming that user biometric data packets are completely deleted from active caches immediately after session verification.\n\nINPUT, the user will paste:\n- The New York consumer endpoint inventory and the biometric data stores each one touches (caches, message queues, object storage, log sinks, CDN, backups)\n- The TTL or retention rule configured on each store\n- The firm's stated post-session biometric erasure policy\n- Any records-retention obligation that conflicts with immediate erasure (AML decision evidence, audit trail, prior SAR support)\n- Any prior audit or exam finding on biometric data persistence\n\nOUTPUT, return the following structured response:\n\n1. BIOMETRIC RESIDENCY MAP\nFor each store a biometric packet can reach:\n- The store, the endpoint that writes to it, and the biometric artifact it holds (face template, liveness frame, biometric hash)\n- The configured TTL or retention rule\n- Whether the packet is written in raw, hashed, or reversible form\n\n2. ERASURE VERDICT TABLE\nFor each store:\n- Verdict (purged-on-session, delayed-purge, persistent)\n- The TTL, log, or backup evidence that drove the verdict\n- The exact lag between session close and confirmed deletion\n\n3. RETENTION-CONFLICT INVENTORY\n- Each store where the Erie County erasure duty collides with a records-retention obligation\n- The separation the firm should hold: the biometric identifier to purge versus the decision record, rationale, and non-reversible reference to retain\n- The retention schedule change required and the obligation it must still satisfy\n\n4. REMEDIATION PROPOSAL\nFor each persistent or delayed-purge store:\n- The proposed purge mechanism (TTL change, on-session-close delete hook, log redaction, backup exclusion) and the agent or team that owns it\n- The verification check that proves the data is gone after deployment\n- Deployment risk and the rollback path\n\n5. EVIDENCE TRAIL\n- The deletion receipts the firm should retain to prove erasure to a regulator without re-storing the biometric data\n- The audit-trail fields that demonstrate immediate post-session erasure across the New York endpoint set\n- Open questions for compliance and outside counsel on the Erie County retention boundary\n\nBe specific. Name the store IDs and endpoint paths where the audit surfaces persistent biometric data. Do not hedge on a persistent-data call.

Test it in Claude or another LLM

This prompt is built for the Arbiter agent inside deepidv, where it runs an erasure-verification loop across your live New York consumer endpoints and proves that biometric data packets clear from active caches the moment session verification closes, as the Erie County Biometric Privacy Act requires. Here is how to dry-run the same workflow in any general LLM with synthetic data before you point Arbiter at live endpoints.

1
Paste the full prompt into Claude, ChatGPT, or Gemini, and replace the direct address 'Arbiter, run an audit loop...' with a role instruction such as 'Act as a privacy red-team engineer auditing our NY consumer endpoints for immediate post-session biometric erasure under the Erie County Biometric Privacy Act.' Keep the five OUTPUT sections (residency map, erasure verdict, retention-conflict inventory, remediation, evidence trail) intact.
2
Below the prompt, paste the synthetic sample data block from sampleInput so the model has fake endpoint paths, the biometric stores each one touches, a stated erasure policy, a TTL per store, and a conflicting retention obligation to reason over. This stands in for the live deepidv endpoint and data-store inventory.
3
Add a line instructing the model to name the exact fake store IDs wherever it surfaces persistent biometric data and to not hedge on a persistent-data call, mirroring the prompt's closing instruction.
4
Good output for this prompt is a residency map naming every store that touches a biometric packet; an erasure verdict table rating each store purged-on-session, delayed-purge, or persistent with the TTL or log evidence behind the call; a retention-conflict section that explicitly separates the biometric identifier from the decision record; and remediation items each naming an owning agent, the purge mechanism, and a verification check. If it returns generic privacy advice without naming the fake store IDs or the per-store verdicts, sharpen the data block and re-run.
5
Once the output shape looks right, run the prompt live in the deepidv dashboard where Arbiter executes the loop against your real NY consumer endpoints, cache TTLs, log sinks, and backup snapshots.

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.

NY CONSUMER ENDPOINTS (synthetic, fake):
- /v1/ny/session/liveness | stores: CACHE-TEST-REDIS-00, QUEUE-TEST-KAFKA-01
- /v1/ny/session/match | stores: CACHE-TEST-REDIS-00, LOG-TEST-SINK-02
- /v1/ny/session/result | stores: OBJ-TEST-S3-03, BACKUP-TEST-SNAP-04
STORE TTLs (fake): CACHE-TEST-REDIS-00 = 3600s; QUEUE-TEST-KAFKA-01 = 7 day retention; LOG-TEST-SINK-02 = 30 day retention (logs full request body incl. biometric hash); OBJ-TEST-S3-03 = no expiry rule; BACKUP-TEST-SNAP-04 = nightly, 35 day rolling
STATED ERASURE POLICY (fake): 'biometric packets deleted immediately post-session verification'
RETENTION CONFLICT (fake): AML decision evidence must be retained 5 years per internal policy POLICY-TEST-RET-00
PRIOR FINDING (fake): AUDIT-TEST-0000 noted liveness frames recoverable from BACKUP-TEST-SNAP-04 nine days after session close

Pairs with on deepidv

Sources & further reading

FAQ

What does the Erie County Biometric Privacy Act require for biometric data erasure?

The act sets strict limits on how long an operator may hold a consumer's biometric identifiers and requires deletion once the purpose for collection is met. For a verification flow, that purpose closes when the session completes, so the face template, liveness frames, and biometric hashes should clear from active stores immediately after, not sit in caches, queues, or logs. This prompt audits every place that data can persist and proves whether each one purges on session close or holds the data past its lawful purpose.

How does biometric erasure collide with my records-retention duties?

A verification program often has to retain decision evidence for an audit trail or a downstream SAR, while the Erie County act pushes you to delete the biometric identifiers themselves. The resolution is to retain the decision, the rationale, and a non-reversible reference, while purging the raw biometric packet. The prompt surfaces each conflict explicitly so compliance and outside counsel can set a retention schedule that satisfies both obligations rather than picking one and breaching the other.

Can I run this erasure audit outside the deepidv dashboard?

Yes. The prompt is written for Arbiter, the deepidv red-team agent, but it works in Claude, ChatGPT, or Gemini as a structured architecture review if you paste your endpoint inventory and data-store map into the INPUT section. You lose the live cache and log inspection that way, so treat external runs as a point-in-time design audit rather than proof that data actually cleared from production.

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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