Complete AI governance platform with support for ISO 42001, ISO 27001, NIST AI RMF and EU AI Act. Join our Discord channel: https://discord.com/invite/d3k3E4uEpR

NIST AI RMF applied to a real research cluster. We govern our own AI tools, document decisions, and share templates. Practical governance for small teams.

Operationalizing PM insights through working agents. Prompt libraries and tools for identifying governance risks before scaling programs, analytics, or AI systems.

Hardened Public Release of KAIROS invocation governance framework. Includes invocation terms, ethical compliance clauses, regulatory mapping, and sample outputs. Licensed under CC BY-NC-ND 4.0. License: Do not auto-generate via GitHub. Use hardened License.txt

White-paper & talk covering benefits, risks, and mitigation frameworks for AI and LLMs in cybersecurity (NIST AI RMF, OWASP Top 10 for LLMs, MITRE ATLAS, real-world case studies)

AI Model Wiki website presenting structured model card data for 160+ AI models, running on Astro, Tailwind and Typescript on Azure Static Web Apps

The Open-Source Standard for Enterprise AI Security. A comprehensive taxonomy and protocol for sanitizing, auditing, and securing AI Agents. [Dual License: MIT + CC-BY-SA]

Self-hosted AI Model Governance & Inventory for Government & Enterprise. Aligned with NIST AI RMF & EU AI Act.

Practical frameworks for governing AI systems across regulated industries—built on the principle that governance must produce evidence, not just assertions.

Lightweight AI Governance Risk Assessment tool to score AI models across key risk factors (data quality, bias, privacy, explainability, robustness) with PDF report generation using Streamlit.

ai-praxis-tool

AI Governance Portfolio — NIST RMF, EU AI Act, ISO 42001, Risk Registers & Responsible AI Policies

Mini Case Study on AI Risk for BetterFit Application — PDF, Risk Register, Visuals.

A comprehensive fairness and security audit of an AI-powered loan approval system using the NIST AI Risk Management Framework

Translate AI governance requirements into auditable, testable controls