# ZTDS.ai — Evidentiary Sovereignty and Forensics Specification
## Normative Conformance Specification for Crypto Asset Investigations, AML Intelligence and Judicial Chain of Custody (RFC v1.0 Extension)

Standards Authority: ZTDS AI Consortium & Standards Authority (BrandMeWeb Ecosystem)
Lead Author & Chief Architect: Ilya Sibiryakov (ORCID: 0009-0002-0642-5985)
Document ID: ZTDS-SPEC-2026-FORENSIC-V1
Status: Canonical Technical & Legal Specification · Standards Track Extension
Effective Date: 02 October 2026
Permanent Repository Anchor: https://ztds.ai/docs/legal/ZTDS_Evidentiary_Sovereignty_and_Forensics_Specification.txt

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1. EXECUTIVE SUMMARY & THE WEB3 ARCHITECTURAL PARADOX
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The foundational ethos of decentralized cryptography and blockchain intelligence
is encapsulated in a single non-negotiable axiom: "Don't trust, verify." In the
financial crime, anti-money laundering (AML), and crypto-forensic domains, no
certified investigator would ever entrust private keys, investigative clusters,
or seed phrases to unverified third-party promises.

Yet, with the rapid enterprise adoption of Generative Artificial Intelligence (GAI)
and autonomous agent workflows, the industry has encountered a profound systemic
paradox:
1. Forensic analysts, compliance officers, and financial intelligence units (FIUs)
   paste raw suspect wallet addresses, unconfirmed transaction links, peel-chain
   graphs, and confidential Suspicious Activity Report (SAR / STR) drafts
   directly into external cloud-hosted Large Language Models (e.g., OpenAI,
   Microsoft Azure, Anthropic).
2. Organizations rely upon corporate Data Processing Agreements (DPAs) and
   administrative settings (such as "do not train on customer data") as
   substitute security perimeters.
3. In doing so, organizations surrender Evidentiary Sovereignty—physically
   transmitting unadjudicated suspect identifiers, proprietary cluster tags, and
   investigative working hypotheses into multi-tenant remote memory outside local
   jurisdictional custody.

This specification formalizes the ZTDS Evidentiary Sovereignty Profile
(ZTDS-FORENSIC), establishing mathematical and operational requirements that
enable investigators to leverage the full reasoning and topological intelligence
of leading language models while guaranteeing absolute zero data egress
(Delta Egress = 0.00 B) and providing a cryptographically verifiable
Chain-of-Custody Docket Receipt.

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2. THREAT MODEL: THE FOUR VECTORS OF INVESTIGATIVE DEGRADATION
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Conforming implementations must defend against four distinct attack and exposure
vectors:

2.1. Vector 1: Unauthorized Third-Party Ingestion & Breach of Statutory Secrecy
Under statutory financial intelligence regimes—including the US Bank Secrecy Act
(BSA, 31 U.S.C. Section 5318(g)(2)) and equivalent international regulations—the
unauthorized disclosure of a SAR or of the fact that an investigation is underway
is a statutory felony ("tipping off"). Transmitting raw SAR narratives or target
wallet clusters to commercial cloud APIs creates an external copy stored on
third-party infrastructure, presenting direct regulatory liability.

2.2. Vector 2: Subpoena & Third-Party Discovery Exposure
In criminal prosecutions and contested civil asset forfeiture proceedings, defense
counsel routinely subpoena external cloud service providers for all prompt logs,
interaction histories, and model completions associated with the investigative team.
If raw suspect addresses or draft theories were submitted to external models:
* Defense counsel can challenge investigative bias, exploratory hallucinations,
  or procedural irregularities.
* Premature disclosure of unindicted co-conspirators or confidential informants
  embedded in prompt context can compromise parallel operations.
* A signed DPA does not shield an enterprise from federal subpoenas, national
  security letters, or cross-border discovery orders served directly upon the
  cloud provider.

2.3. Vector 3: Evidentiary Chain of Custody Degradation
Under US Federal Rules of Evidence (FRE 901/902), FRE 502, and international
equivalents, evidence derived from computational processing must maintain an
unbroken, verifiable chain of custody. When cleartext evidence is processed by
proprietary, non-deterministic third-party black-box models:
* The investigator cannot mathematically prove that the model did not interpolate,
  fabricate, or cross-contaminate data with other multi-tenant enterprise sessions.
* The integrity of the evidentiary chain is vulnerable to severe challenge
  during cross-examination.

2.4. Vector 4: On-Chain Correlation & Heuristic Re-Identification
Masking only public keys (e.g., converting 0x71C... to [WALLET_1]) does not
prevent re-identification if high-precision transaction amounts (e.g.,
14.89214712 ETH) or exact block timestamps are submitted in cleartext. An
adversary or untrusted cloud provider can query public block explorers (Etherscan,
Mempool, Blockchain.info) and trivially recover the masked public addresses by
matching unique floating-point outputs.

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3. NORMATIVE CONFORMANCE REQUIREMENTS: THE FOUR FORENSIC INVARIANTS
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To achieve and maintain ZTDS-FORENSIC accreditation, a software implementation,
analytical agent, or investigative workbench must strictly adhere to the
following four normative invariants:

Invariant F-1: Zero External Egress of Raw Target Entities (Delta Egress = 0.00 B)
Zero bytes of unmasked blockchain addresses, transaction hashes, smart contract
addresses, raw private keys, SAR IDs, FIU case numbers, or cluster tags may be
transmitted across the network interface. Sanitization must execute strictly on
the investigator's local host in volatile RAM before socket serialization.

Invariant F-2: Directed Acyclic Graph (DAG) Structural Tokenization
The sanitization engine must substitute target entities with syntactically stable,
context-preserving bracketed surrogates:
* Public Addresses: [TARGET_WALLET_1], [COUNTERPARTY_WALLET_2], [INTERMEDIARY_HOP_3]
* Transaction Hashes: [TX_HASH_1], [TX_HASH_2]
* Clusters & Mixers: [MIXER_POOL_1], [CLUSTER_ALPHA_1]
* Case Identifiers: [SAR_ID_1], [CASE_DOCKET_1]
The tokenization must preserve the topological structure of fund flows (e.g.,
peeling chains, smurfing patterns, nesting exchanges), allowing the upstream LLM
to analyze the typology and draft investigative summaries without learning any
cleartext identifiers. Local reconstitution occurs strictly inside local
workstation volatile memory upon receiving the model output.

Invariant F-3: Value Quantization & Temporal Clamping (Anti-Correlation)
Conforming implementations must implement automated anti-correlation sanitization:
1. Logarithmic Amount Binning: Floating-point transaction values must either be
   substituted with abstract value surrogates ([TX_VALUE_1]) or clamped into
   order-of-magnitude ranges (e.g., [RANGE_10_TO_50_ETH]).
2. Temporal Window Clamping: Exact Unix timestamps or block heights must be mapped
   to relative or discretized chronological intervals (e.g., [WINDOW_T0_PLUS_2H],
   [EPOCH_2026_Q3]) to prevent block-height correlation against public ledgers.

Invariant F-4: Verifiable Ephemeral In-RAM Execution
Session mapping tables R = {(m_i, s_i)} must reside solely in non-swappable
volatile memory (mlock or sandboxed WebAssembly linear memory). Writing session
maps to secondary storage (disk caches, browser storage, unencrypted swap) is
strictly prohibited. Memory zeroization must be executed immediately upon
completion of the investigative session.

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4. CRYPTOGRAPHIC CHAIN-OF-CUSTODY DOCKET RECEIPT (Ed25519)
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All conforming forensic implementations must mint an asymmetric Ed25519 (RFC 8032)
Chain-of-Custody Docket Receipt prior to dispatching prompts to external models.
The receipt includes:
- Canonical timestamp in UTC.
- SHA-256 hashes of raw and sanitized payloads.
- Quantitative audit of masked entities (wallets, TxHashes, case IDs).
- Confirmation of active Amount Binning and Temporal Clamping.
- Asymmetric Ed25519 digital signature verifiable offline without network access.

Attaching this cryptographic receipt to a SAR filing or criminal evidence docket
provides mathematical proof that no unadjudicated target records or confidential
lead narratives exited local custody.

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5. STATUTORY & REGULATORY MAPPING
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- US Federal Rules of Evidence (FRE 901 & 902): Self-authenticating evidentiary
  integrity; preserves admissibility under computational processing.
- US Federal Rules of Evidence (FRE 502(b)): Prevents inadvertent waiver of
  attorney-client and work-product privilege.
- US Bank Secrecy Act (BSA, 31 U.S.C. Section 5318(g)(2)): Eliminates statutory
  "tipping off" liabilities in AI-assisted suspicious activity analysis.
- EU Anti-Money Laundering Directive (AMLD6, Directive 2018/1673): Preserves
  strict FIU confidentiality rules.
- EU GDPR Article 28 & Recital 26: Eliminates third-party processor status;
  topological surrogate prompts fall outside personal data scope.
- FATF Recommendation 15: Implements provable, risk-based AI security safeguards
  for virtual asset service providers and law enforcement bodies.