A beginner-friendly AI Governance & Risk Toolkit — risk register, governance templates, and audit-ready workflows for early-stage AI teams.

A practical framework for turning data analysis into decision policies you can defend. Covers risk modeling, thresholding, exception handling, policy cards, monitoring, and update triggers, using real patterns like abstention rules, reorder points, and fairness-aware benchmarking. Built for “ship it” data science.

Forkit Core is an open source passport layer for AI models and agents with GitHub CI validation, local verification, and Hugging Face-compatible export.

Automated validation toolkit for tabular ML models in finance and regulated domains.

Audit-ready explainability artifacts (reason codes, model cards, drift checks) for scikit-learn investment & credit-risk models.

A governed local AI build-and-memory system that trains small brains, compares them, protects the better one, archives the worse one, and preserves the evidence of why. v1.0.0-governed.

Data Trust Engineering (DTE) is a vendor-neutral, engineering-first approach to building trusted, Data, Analytics and AI-ready data systems. This repo hosts the Manifesto, Patterns, and the Trust Dashboard MVP.

EU AI Act governance prototype that turns real medical AI evidence and ISO/IEC 42001 governance scaffolding into reviewable classification, evidence checks, human oversight, and incident-handling paths.

Depth-tracked regulatory audit primitives for privacy-preserving AI audits with signed envelopes and TenSEAL CKKS support.

Four Tests Standard (4TS) - Vendor-neutral specification for verifiable AI governance

Enterprise AI Router and Governance System — the AI that governs all AIs

Regime-based evaluation framework for financial NLP stability. Implements chronological cross-validation, semantic drift quantification via Jensen-Shannon divergence, and multi-faceted robustness profiling. Replicates Sun et al.'s (2025) methodology with modular, auditable Python codebase.

Supporting materials for “Building Governable ML Models with R,” presented at posit::conf 2025

Model governance for insurance pricing — PRA SS1/23 validation reports, model risk management, risk tier scoring

SPAR (Sovereign Physics Autonomous Review) - Claim-aware review framework for systems whose outputs can pass while their claims drift.

Customizable AI Acceptable Use Policy and governance framework for US enterprises. MIT licensed. Covers compliance, HR, infosec, and legal.

Domain models documentation and governance. A live reference for humans, tools and AI agents collaboration on data meaning.

This repository defines a reproducible Layer-0 functional compliance specification for Large Language Models.

Prompt-governed CLI automation architecture for AutoFACS: bounded execution, audit-first workflows, human-reviewed operations, and public-facing documentation for CV/ML control-plane design.

Drift observability architecture for Databricks Delta Lake — detects data & model drifts, builds PSI visualizations, and exports governance telemetry for Responsible AI.