AI Evidence Integrity / Tamper-Evident Audit Ledger Playbook

版本: v1.0 日期: 2026-06-30 适用对象: experienced CBAP、financial retail PM、product architect、enterprise architect、AI governance lead、audit-risk partner、privacy / legal / records partner、platform owner。

本文是一份执行型手册, 用于把 AI 证据对象、哈希、签名、append-only ledger、provenance claims、attestation chain、segregation of duties、redaction、legal hold 和 audit queries 设计成可落地的 evidence trustworthiness architecture。它不替代记录保留制度、运行观测方案、流程审计、监管报告签署或 AI BOM；它专注于证明证据本身是否可信、是否可验证、是否未被静默改写。

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1. Purpose And When To Use

Purpose

本手册帮助团队在 AI 产品上线前回答六个问题:

Use this playbook when

Do not use this playbook as a general observability plan. Observability tells teams what happened technically; this playbook proves whether the evidence used to explain what happened can be trusted.

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2. Operating Model

2.1 Roles and accountabilities

Legend: R = responsible, A = accountable, C = consulted, I = informed.

2.2 Cadence

2.3 Control principles

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3. Template: Evidence Object Schema

Use this schema for high-impact evidence. Low-risk internal drafts can use a lighter profile, but should preserve evidence_id, event_type, created_at, producer, payload_hash, version_set and ledger_position.

Schema acceptance criteria:

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4. Template: Ledger Event Taxonomy

Use a controlled taxonomy so audit queries can test populations consistently.

Taxonomy rule:

If an event can change what a customer, investigator, underwriter, dispute operator, board member or regulator sees, it must have a distinct ledger event rather than being hidden inside an untyped log line.

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5. Template: Attestation Matrix

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6. Template: Tamper-Evidence Test

Run these tests before pilot, before high-risk release and after major architecture changes.

Execution rule:

A high-impact release cannot pass evidence integrity readiness if TE-001 through TE-007 fail. TE-008 and TE-009 are mandatory when redaction or legal hold applies.

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7. Template: Legal Hold / Redaction Rule

7.1 Legal hold rule

7.2 Redaction rule

Boundary rule:

Redaction reduces exposure for review and MI; it does not destroy the original evidence chain. Legal hold can preserve both original and derivative objects depending on scope.

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8. Template: Audit Query Pack

Create these queries as saved catalog items before release. The exact SQL or graph query depends on platform, but each query must have population, join logic and owner.

Audit query acceptance criteria:

• Query owner can run it without delivery-team manual filtering.
• Query result includes evidence ids, event ids, business object hash, owner and exception classification.
• Query distinguishes control failure, documentation defect, justified exception and out-of-scope event.
• Query output is itself saved as a signed evidence object for release readiness.

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9. Template: Release Evidence Packet

Use this packet for high-impact AI release or material change.

Minimum release packet statement:

The release evidence packet for rel_kyc_decline_assist_20260630 contains signed evidence objects, verified hashes, attestation mappings, audit query results, redaction and legal hold rules, and one complete replay sample. The AI system may assist KYC reviewers with evidence organization and rationale drafting. It may not autonomously decline customers or send customer communications. Blocking evidence integrity queries returned zero unresolved failures.

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10. PM / BA / Architecture Questions

PM questions

BA / CBAP questions

Architecture questions

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11. Release Checklist

Release decision rule:

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12. Executive Narrative

Use this narrative for steering committee, board risk committee or audit-risk partner conversations.

We designed the AI evidence integrity control as a platform-level trust mechanism, not a manual document pack. For high-impact AI workflows, each critical prompt, source retrieval, model output, policy decision, approval, tool action, customer communication and management metric becomes a structured evidence object. The object is hashed, signed by the producing service, written to an append-only ledger and linked to its provenance and attestation chain.

This means we can independently verify whether evidence was changed after creation, whether the human approved the exact output or tool input, whether redacted review material still derives from the original evidence, and whether legal hold freezes the right population. The design minimizes raw sensitive data in the ledger by storing hashes and restricted pointers, while preserving the ability to reconstruct a case under controlled access.

For release readiness, we do not rely on assurance by assertion. We run audit queries for missing evidence, failed hashes, invalid signatures, approval mismatches, segregation-of-duties breaches, unauthorized tool actions, redaction defects and legal hold conflicts. These results are themselves signed into the release evidence packet. The outcome is a defensible evidence chain for AML, KYC, credit policy advice, payment dispute handling, customer communication and board MI.

Board-level one-liner:

We can prove not only what the AI system did, but whether the evidence used to prove it remained intact, attributable, approved, redacted and preserved.

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13. Interview Drills

Drill 1: Explain evidence integrity in 30 seconds

Strong answer:

Evidence integrity means the AI system's evidence can be independently trusted. I would create structured evidence objects, hash and sign them, store them in an append-only ledger, link them through provenance claims and verify approvals, redactions and legal holds through attestation. This is different from ordinary logs because it proves the evidence was not silently altered and that reviewers approved exact content.

Drill 2: How would you design it for a KYC decline assistant?

Strong answer:

I would separate the AI recommendation from the final human decline decision. Document extraction, sanctions results, policy references, AI rationale, reviewer decision and customer communication each become evidence objects. The final decline decision must reference the AI recommendation hash only as support, not as autonomous decision authority. Customer challenge replay can then show original document hashes, extraction version, policy version, reviewer attestation and the exact customer message sent.

Drill 3: How do you avoid storing too much sensitive data?

Strong answer:

The ledger stores hashes, pointers, classification, version set and attestations, not every raw document. Raw evidence stays in a restricted vault. Redacted exports are derivative evidence objects linked to original hashes. Access, export and legal hold are also ledger events, so privacy control becomes part of the evidence chain.

Drill 4: What is approval binding?

Strong answer:

Approval binding means the reviewer decision references the exact evidence hash, output hash or tool input hash visible at review time. If a payment dispute tool executes a different input than the checker approved, the verifier or audit query flags it as invalid. This prevents generic approvals from being reused for changed content.

Drill 5: What would make you reject a release?

Strong answer:

I would reject a high-impact release if hashes fail, signatures cannot be verified, customer-impacting tool actions lack policy decision, approvals are not bound to exact inputs, SoD is breached, legal hold behavior is untested, or replay cannot reconstruct a representative customer-impacting case.

Drill 6: How is this different from OpenTelemetry?

Strong answer:

OpenTelemetry is excellent for traces, spans, metrics and logs. I would use its trace ids to connect technical execution to evidence events. But audit trust requires signed evidence objects, append-only ledger records, approval attestations, redaction lineage and legal hold state. Observability explains runtime behavior; evidence integrity proves the evidence chain is trustworthy.

Drill 7: How would you handle board MI?

Strong answer:

Board MI should have a signed population snapshot, query hash, data source attestation, metric output and reviewer approval. If numbers are adjusted, the adjustment is a correction event, not an overwritten slide. Redacted board packs link back to restricted evidence populations, so management can rely on metrics without exposing raw customer data.

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14. Source Anchors

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15. Self-Check