Four Tests Standard (4TS) - Verifiable AI Governance

Version: 1.0.2
Steward: FERZ LLC
License: CC BY-NC-ND 4.0 (specification), MIT (schemas/test vectors)

The TCP/IP layer for AI governance
Ensure consequential AI decisions are stop-capable, owned, replayable, and escalatable—by design.

📄 Academic Publications

📖 Foundational Paper

Verifiable AI Governance: The Four Tests Standard (4TS) and Proof-Carrying Decisions
Edward Meyman | October 2025

Available on:

• SSRN (Primary)
• ResearchGate

Establishes formal specification, theoretical foundations, and proof of necessary and sufficient conditions for verifiable AI governance.

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How to Cite:

Meyman, E. (2025). Verifiable AI Governance: The Four Tests Standard (4TS) 
and Proof-Carrying Decisions. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5688982

Overview

The Four Tests Standard (4TS) is a vendor-neutral technical specification for verifiable AI governance. It enables organizations to prove that AI systems in regulated industries meet compliance requirements through:

• Proof-Carrying Decisions (PCDs): Canonical JSON objects encoding all information needed to verify governance at decision boundaries
• Deterministic Verification: Mathematical acceptance criteria that produce consistent results independent of who verifies
• Two Replay Modes: State-Replay for byte-exact reproduction, Protocol-Replay for gate-based validation
• Fail-Closed Design: Actions blocked by default unless approval can be cryptographically proven

The Four Tests

Test	Requirement	Enforced Through
STOP	System can be halted before side-effects	Effect-token issuance gated by approval
OWNERSHIP	Identified authority signs policy before execution	Cryptographic signatures with timestamp ordering
REPLAY	Decision can be reproduced at boundary	State-Replay or Protocol-Replay modes
ESCALATION	Mandatory custody transfer on denial/thresholds	Explicit routing with human-in-loop paths

Quick Start

Enable auditors to verify AI compliance mathematically rather than through sampling—deterministic verification at decision boundaries.

Installation

# Clone the repository
git clone https://github.com/edmeyman/4ts-standard.git
cd 4ts-standard

# Install dependencies (Python 3.10+)
pip install -r requirements.txt

Validate Your First PCD

# Validate against test vectors
python tools/validator/quickstart_validate.py --json examples/model-deployment-state-replay.json

# Expected output: PASS

Create Your First PCD

from tools import pcd_builder

pcd = pcd_builder.create_pcd(
    boundary="deploy",
    artifacts={"models": [{"id": "my-model-v1.0", "sha256": "..."}]},
    replay_strategy="state"
)

print(pcd.to_json())

Repository Structure

4ts-standard/
├── README.md                    # This file
├── SPECIFICATION.md             # Complete technical specification (§§0-11)
├── LICENSE.md                   # Dual license (CC BY-NC-ND 4.0 + MIT)
├── CHANGELOG.md                 # Version history
├── CONTRIBUTING.md              # How to contribute
│
├── schemas/                     # JSON Schema definitions
│   ├── pcd.schema.json         # PCD structure (draft 2020-12)
│   └── verifier.config.schema.json  # Verifier configuration
│
├── examples/                    # Concrete PCD examples
│   ├── model-deployment-state-replay.json
│   ├── rag-system-protocol-replay.json
│   ├── llm-tool-inference.json
│   └── etl-pipeline-batch.json
│
├── test-vectors/                # Conformance test suite
│   ├── positive/               # Must PASS (8 vectors)
│   │   ├── PCD-A1_state_auto_approve.json
│   │   ├── PCD-A2_protocol_with_gates.json
│   │   └── PCD-A3_fail_closed_denial.json
│   └── negative/               # Must FAIL with specific errors (5 vectors)
│       ├── NC-1_posthoc_signature.json
│       ├── NC-2_missing_custody.json
│       ├── NC-3_untyped_lineage.json
│       ├── NC-4_side_effect_on_denial.json
│       └── NC-5_protocol_gate_fail.json
│
├── tools/                       # Reference implementations
│   ├── validator/              # Python reference verifier
│   │   ├── quickstart_validate.py
│   │   └── verifier.py
│   └── canonicalizer/          # JSON canonicalization
│       └── canonicalize.py
│
└── docs/                        # Additional documentation
    ├── quickstart.md           # Getting started guide
    ├── implementation-guide.md # Detailed implementation patterns
    ├── faq.md                  # Frequently asked questions
    └── error-catalog.md        # Complete error code reference

Use Cases

4TS supports diverse AI deployment patterns:

Profile	PCD Emission	Replay Mode	Key Considerations
LLM Tools	Per tool action with external effects	State or Protocol	Typed lineage for tool I/O
RAG Systems	Per response triggering workflows	Protocol (frozen index)	Gates on answerability/attribution
Model Deployment	At deployment and policy changes	State or Protocol (eval gates)	Pre-exec policy signature required
BPMN/ETL	Per job with external writes	State or Protocol	Compensating actions for rollbacks
Agentic Systems	Per plan execution	Protocol with explicit gates	Sub-PCDs for high-risk steps

Conformance

To claim 4TS conformance, implementers must:

1. Pass all test vectors: 3 positive (PASS), 5 negative (expected failures with correct error codes)
2. Publish conformance claim:

   Tool@Version • PCD-1 • Bundle-1.0.2 • 8/8 • sha256:manifest_hash • logs_link
   

3. Implement core verification: PCD schema validation, signature verification, replay logic, fail-closed enforcement

See SPECIFICATION.md §7 for complete conformance requirements.

Implementation Profiles

Healthcare/Life Sciences

• Boundary: Model deployment for diagnostic/treatment decisions
• Replay: State-Replay with frozen training/test sets
• Gates: AUROC, calibration error, demographic parity
• Regulatory: FDA 510(k), EU MDR alignment

Financial Services

• Boundary: Inference-level for credit/trading decisions
• Replay: Protocol-Replay with deterministic risk metrics
• Gates: Accuracy, fairness (disparate impact), attribution
• Regulatory: SR 11-7, ECOA, MiFID II alignment

Government/Defense

• Boundary: Deploy and policy-change for mission-critical systems
• Replay: State-Replay with air-gapped verification
• Gates: Security clearance checks, operational safety thresholds
• Regulatory: NIST AI RMF, DoD AI principles

Documentation

• Complete Specification - Full technical standard (§§0-11)
• Quick Start Guide - 10-minute implementation tutorial
• Implementation Guide - Detailed patterns and best practices
• FAQ - Common questions and answers
• Error Catalog - All error codes with triggers and recovery

Community & Support

• Issues & Bugs: GitHub Issues
• Discussions: GitHub Discussions
• Email: contact@ferzconsulting.com
• Website: https://ferz.ai

Contributing

We welcome contributions! Please see CONTRIBUTING.md for:

• Code of conduct
• How to submit issues and pull requests
• Development workflow
• Testing requirements

Related Standards & Documents

• Deterministic AI Governance - Executive Guide - Business rationale and minimum governance bar
• JSON Schema draft 2020-12 - Schema specification standard
• RFC 3339 (ISO 8601) - Timestamp format
• SHA-256 (FIPS 180-4) - Cryptographic hashing
• EdDSA/ECDSA - Digital signature algorithms

Version History

Version	Date	Changes
1.0.0	2025-09	Initial release with core standard
1.0.1	2025-10	Added adoption profiles, clarified gate format, expanded error codes
1.0.2	2025-11	Added traceability matrix, glossary, concrete PCD examples

See CHANGELOG.md for detailed changes.

License

• Specification Text: CC BY-NC-ND 4.0

  • Attribution required
  • Non-commercial use
  • No derivatives

• Schemas & Test Vectors: MIT License

  • Permissive use for implementation

See LICENSE.md for complete terms.

For commercial licensing inquiries: contact@ferzconsulting.com

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© 2025 FERZ LLC | Vendor-neutral open standard for verifiable AI governance