Tamper-Evident Evidence Vault & Custody Mechanics

AI governance programs increasingly depend on record-keeping, traceability, and lifecycle oversight, yet the underlying evidence is often stored in places optimized for operations rather than custody. Logs roll over, dashboards mutate, tickets separate approvals from technical state, and ordinary data stores preserve bytes without preserving an independently verifiable chain of custody.

High-stakes AI requires a dedicated Evidence Vault: a custody layer that canonicalizes evidence objects, applies cryptographic integrity controls, preserves linkage across lifecycle events, and reconstructs inspection-ready audit packs without requiring the verifier to trust the originating application.

This technical specification details the architecture, ingestion flows, cryptographic primitives, and database-backed custody contract of the Tamper-Evident Evidence Vault.

1. Architectural Positioning: SIEM vs. Vault

The Evidence Vault is not a generic data lake, not a conventional Security Information and Event Management (SIEM) tool, and not merely raw, immutable cloud storage. It is the structural substrate that turns ephemeral lifecycle receipts and supporting artifacts into portable, self-describing evidence objects.

2. Cryptographic Custody Mechanics & Sealing Pipeline

The Evidence Vault enforces a multi-stage, zero-trust ingestion pipeline that seals evidence the moment it is received. The architecture ensures that even a database administrator with full write privileges cannot retroactively modify or forge a past record without detection.

The Sealing Pipeline Step-by-Step

• Ingestion & Isolation: Receipts, policy versions, and evidence objects are received from upstream subsystems (such as MLOps pipelines, agent tool wrappers, shadow AI discovery proxies, or privacy handlers).
• Canonicalization (JCS): Payload schemas are normalized according to the RFC 8785 JSON Canonicalization Scheme (JCS) version ciaf-json-v1. This resolves all ordering, floating-point, whitespace, and Unicode ambiguities, guaranteeing that the exact same semantic payload re-computes to the exact same hash on any downstream system.
• Hashing: The canonicalized byte stream is hashed using SHA-256 to generate the primary content_hash.
• Signing: Where the organization's Assurance Profile requires signature metadata, the hash is signed using an Ed25519 private key (either managed via KMS or local Hardware Security Module). This generates a deterministic signature, verifying the identity of the asserting system without relying on random seeds.
• Storage Splitting: The metadata and linkage records are written to high-performance relational database indexes. Large payloads and raw artifacts are saved in immutable WORM (Write-Once-Read-Many) cloud storage buckets, with their storage-content hashes logged directly in the relational row.
• Sealing: Receipts are batched and sealed. High-assurance records are wrapped into an immutable vault_objects envelope containing explicit retention policies, signature metadata, Merkle root reference, and compliance classification tags.

3. Merkle Batching & Inclusion Proofs

To avoid the performance and cost bottleneck of anchoring individual receipts directly to high-latency external ledgers or write-once cold storage, the Evidence Vault uses Merkle Trees to batch receipts dynamically.

This structure is backed by two relational tables: receipt_batches and receipt_batch_items.

• receipt_batches acts as the Merkle tree root register. It defines the merkle_root (the SHA-256 hash representing the absolute cryptographic state of the entire batch) and the total_receipts count.
• receipt_batch_items maps individual receipts to their respective batches. It stores the receipt ID and a compact merkle_proof JSON array containing the sibling hashes and leaf index.

The Power of Merkle Inclusion Proofs

This Merkle batching mechanism solves the tension between auditability, performance, and confidentiality:

• Log-Scale Efficiency: A verifier can prove that an individual receipt R1 (with hash H_a) was part of a verified batch of 1,000,000 receipts without downloading the other 999,999 records. They only require the sibling hashes H_b and H_cd to recompute the path up to the anchored Merkle Root.
• Privacy Preservation: An external regulator can verify the authenticity and temporal existence of a single, non-sensitive audit receipt without exposing other confidential, proprietary, or privacy-restricted receipts generated inside the same batch.

4. Deterministic Audit Pack Assembly

When an internal auditor, external examiner, or regulatory investigator requests proof of governance, the Evidence Vault does not force them to browse raw databases or search through unstructured logs. Instead, the vault assembles a sealed, self-verifying, and portable Audit Pack.

The Assembly Pipeline:

• Define Scope: The investigator submits a query defining the audit parameters (e.g., "Provide all validation, approval, and deployment evidence for claims-triage-model v4 between Q1 and Q2").
• Retrieve Evidence Candidates: The vault queries the index to isolate the target receipts, policy versions, gate evaluations, and linked evidence objects.
• Validate Lineage Graph: The system walks the receipt_links and ai_lifecycle_object_links to compile the complete ancestry graph. It ensures that no dataset root, training run, or validation check is missing from the chain.
• Capture Snapshots: Rather than linking to live, mutable database rows, the vault writes static snapshots of each included item into the audit_pack_items table, preserving the precise state of the evidence at the moment of the audit.
• Seal and Sign: The vault computes a global pack_hash over the sorted, canonicalized manifest of items and signs it using the vault authority key, writing the manifest record to audit_packs.
• Export: The bundle is exported as an archival, portable payload (containing JCS JSON receipt blocks, schemas, and signatures) that can be unpacked and validated on an entirely offline computer.

5. Privacy-Aware Custody & Selective Redaction

A standard blockchain or naive immutable ledger is incompatible with modern privacy regulations such as GDPR or the California Consumer Privacy Act (CCPA). Under these frameworks, individuals have explicit data subject rights (DSRs), including the "right to be forgotten" (erasure).

If raw personally identifiable information (PII) is written into an immutable ledger or hard-hashed into a Merkle batch, the organization faces an impossible trap: violate privacy laws by refusing to erase the data, or corrupt the cryptographic ledger by overwriting historical blocks.

The Tamper-Evident Evidence Vault solves this conflict through Purpose-Limited Referencing & Redaction Events:

Technical Countermeasures:

• Hashed Data Subject Identifiers: The data_subject_identifiers table stores only salted cryptographic hashes or encrypted-pointer references (hash_algorithm and identifier_hash) rather than raw names, emails, or government IDs.
• Separate Storage of PII Payload: Raw inputs, prompts, and outputs that may contain PII are never embedded inside the immutable receipts payload. Instead, they are stored as separate evidence_objects or referenced via the personal_data_references index.
• The Redaction Event Pattern: When a valid erasure request is approved, the system deletes the raw payload from its storage bucket but preserves the relational record in privacy_redaction_events. It updates the metadata to log that redaction occurred, while leaving the original receipt's cryptographic signature and position in the Merkle batch completely intact. Under audit, the organization can cryptographically prove that:
  1. The model lifecycle control executed successfully at time T1.
  2. A legitimate data-erasure operation was executed under privacy policy at time T2.
  3. The historical proof of control integrity was not compromised during redaction.

6. Relational Database Schema Contract

The following schema contract defines the active database tables powering the Tamper-Evident Evidence Vault subsystem:

7. Operational REST API

Contributors to the cognitiveinsight.ai platform can implement the Evidence Vault using these three core REST endpoints:

POST /api/v1/vault/evidence/ingest
Content-Type: application/json
X-API-Key: ci_key_prod_hash_178abc34

{
  "receipt_id": "rcpt_7709_fdb9",
  "evidence_type": "model_validation_summary",
  "canonicalization_version": "ciaf-json-v1",
  "hash_algorithm": "SHA-256",
  "payload": {
    "model_id": "claims_triage_v4_3",
    "metrics": {
      "accuracy": 0.942,
      "f1_score": 0.931,
      "bias_ratio": 1.01
    },
    "validated_by": "service_mlops_ci"
  }
}

{
  "evidence_object_id": "ev_obj_9901_8a12",
  "content_hash": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
  "is_sealed": true,
  "sealed_at": "2026-08-12T13:11:32Z"
}

POST /api/v1/vault/audit-packs/assemble
Content-Type: application/json

{
  "pack_name": "Q2_Claims_Model_Audit",
  "scope_filter": {
    "start_time": "2026-04-01T00:00:00Z",
    "end_time": "2026-06-30T23:59:59Z",
    "model_id": "claims_triage_v4_3",
    "required_lifecycle_stages": ["validation", "deployment"]
  }
}

{
  "audit_pack_id": "pack_q2_claims_5501",
  "total_items": 14,
  "pack_hash": "f62b8a7701980cb349af1d3928aee9931b279010abc440d99bc30f9a764dca32",
  "status": "sealed",
  "export_url": "https://vault.cognitiveinsight.ai/exports/pack_q2_claims_5501.zip"
}

POST /api/v1/vault/verify/job
Content-Type: application/json

{
  "target_type": "audit_pack",
  "target_id": "pack_q2_claims_5501",
  "config": {
    "verify_signatures": true,
    "verify_merkle_roots": true,
    "check_lineage_continuity": true
  }
}

{
  "job_id": "vjob_8812_44a1",
  "result_status": "passed",
  "checked_count": 14,
  "passed_count": 14,
  "warnings_count": 0,
  "errors_count": 0,
  "findings": {
    "signature_check": "All 14 Ed25519 signatures verified successfully.",
    "hash_check": "All canonical content hashes match stored state.",
    "merkle_integrity": "Merkle roots verified cleanly up to the batch anchors."
  }
}