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.

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.

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.

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

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

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

A platform that makes your domain model executable and shared across humans, systems and AI agents, so nothing is guessed and work stops being re-done. One explicit, documented model becomes the single ground truth that cuts governance overhead, removes ambiguity, and lets AI act with accuracy instead of approximation.

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

Deterministic stress testing of levered UK rental cashflows under rate and vacancy shocks.

Ethical AI governance framework for multi-model alignment, integrity, and enterprise oversight.

Reference system for model governance: evaluation gates → promotion workflow → serving API → Prometheus/Grafana observability.

Policy-driven model promotion gate evaluator for MLOps release workflows

No description provided.

Formales Protokoll zur epistemischen Output-Governance von KI-Systemen.

Professional AI Security Assurance portfolio demonstrating model supply-chain security, LLM red teaming, static analysis, SBOM validation, risk classification, and governance-aligned AI safety workflows.

This project demonstrates how autonomous AI agents can collaborate under strict validation, guardrails, and human-in-the-loop approval to analyze financial data and produce executive-ready reports, designed for regulated environments such as banking and financial services.

A comprehensive MLOps platform that orchestrates AWS Bedrock models through automated pipelines with real-time monitoring and model governance capabilities.

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

Production-ready MSME Credit Risk Pipeline (V3.0). Solved critical data integrity issues (target/scaling) for 47% AUC lift (0.88). Model implements a hard-cutoff policy based on DPD/Utilization, ensuring portfolio PD drops below the 3.75% break-even threshold.

Governed AI Reasoning Interface

End-to-end ML monitoring and drift detection pipeline designed for audit-ready, regulated production environments.

Autonomy Accountability Framework (AAF) and Autonomy Accountability Index (AAI): a governance architecture for evaluating accountability, control, and operational risk in autonomous AI agent systems.

End-to-end, production-aware fraud detection system with real-time inference, delayed labels, explainability, drift monitoring, retraining, and full audit governance.

Turn data analysis into clear, actionable policies with a framework for defining, monitoring, and revising decision rules.