Document Classification: Internal — CHLOM Confidential Phase: 0 → 1 Version: 0.1 Owner: CrownThrive, LLC Last Updated: 2025-08-08

Section 1 — Model Name & Codename

• Example: AegisScore-v1

Section 2 — Purpose & Scope

• Purpose: Predict compliance risk score using entity features, sanctions data, and optional ZK verification results.
• Scope: Compliance Engine only; outputs consumed by TLaaS licensing logic.

Section 3 — Input Features

• Feature Groups:
• Preprocessing: Normalization, one-hot encoding, binning.

Section 4 — Training Datasets

• Sources: Internal feature store, licensed sanctions/PEP datasets.
• Licenses: Compliant with provider ToS; version-pinned.
• Update Cadence: Monthly retrain or upon drift alert.

Section 5 — Training Recipe

• Framework: scikit-learn + XGBoost.
• Hyperparams: grid search on max_depth, learning_rate, n_estimators.
• Hardware: 8-core CPU, 32GB RAM.

Section 6 — Evaluation Metrics

• Accuracy, Precision, Recall, ROC-AUC.
• Calibration Error.
• Fairness metrics: statistical parity diff, equal opportunity diff.

Section 7 — Limitations & Known Biases

• Potential underrepresentation of certain regions in training data.
• Reliance on partner datasets that may contain historical bias.

Section 8 — Rollback/Decommission Plan

• Rollback to previous stable model if KPIs drop > 10%.
• Archive retired models in Model Registry; tag with

Section 9 — Versioning

• Semantic versioning; stored in Model Registry with hash.

Section 10 — Notes

• All model artifacts signed and SBOM generated.

Testing Strategy — CHLOM Phase 0→1

Document Classification: Internal — CHLOM Confidential Owner: CrownThrive, LLC Last Updated: 2025-08-08

Section 1 — Unit & Property Tests

• ML Pre/Post: Deterministic seeds for reproducibility.
• API Endpoints: Validate schema conformance.
• Feature Transforms: Test against golden feature vectors.

Section 2 — Adversarial ML Tests

• Evasion: Perturb inputs to flip decisions.
• Poisoning: Inject anomalous features to test drift detection.
• Model Stealing: Detect over-query patterns.

Section 3 — Contract/Chain Tests

• Fuzz: Randomized inputs to TLaaS methods.
• Gas/Weight: Ensure under defined limits.
• State-Machine: Verify correct transitions under all conditions.

Section 4 — ZKP Tests

• Proof/Algo Fuzzing: Malformed proofs, oversized proofs.
• Boundary Tests: Max field size, expired parameters.
• Negative Tests: Ensure invalid proofs are rejected.

Section 5 — End-to-End (E2E) Tests

• Synthetic Tenants: Mock entities, feature streams, and proof submissions.
• Fraud Red‑Team Packs: Simulated attacks.
• Chaos Days: API surge, Kafka outage, verifier slowdown.

Section 6 — Test Data Management

• Use anonymized or synthetic data.
• Store fixtures in

Section 7 — CI/CD Integration

• Pipeline Stages: build → test → scan → sign → stage → canary → prod.
• Gates: Block promotion on failed critical tests.

Section 8 — Reporting & Coverage

• Coverage Target: ≥ 80% code coverage; ≥ 95% on critical components.
• Reporting: Daily summary to Slack/Portal; weekly dashboard in Grafana.