PRISM — AI Governance Resource Hub

Practical Resources for Intelligent Systems Management — a curated collection of frameworks, tools, regulations, papers, and open-source projects for responsible and trustworthy AI deployment in regulated industries.

Maintained by Sima Bagheri · LinkedIn · Medium

Focus areas: Enterprise AI governance · LLM deployment safety · Risk management · Regulatory compliance (NIST AI RMF, EU AI Act, ISO 42001) · Release readiness · Incident response

Curation standard

This is a curated resource hub, not a link dump. Resources should be practical, credible, and relevant to AI governance, evaluation, risk management, release readiness, or incident response.

See CURATION.md for:

inclusion and exclusion criteria
source preference rules
freshness review cadence
description-quality standards
contribution review checklist

A blocking link-check workflow runs on pull requests and monthly. REVIEW_STATUS.md tracks manual content freshness separately from URL availability.

Regulatory Frameworks
Risk Management Frameworks
Governance Tools & Platforms
AI Testing & Evaluation
Incident Management
Model Cards & Documentation
Academic Papers
Datasets & Benchmarks
Communities & Organizations
Courses & Learning
My Open-Source Frameworks

Regulatory Frameworks

United States

NIST AI Risk Management Framework — NIST’s official hub for the voluntary framework organized around Govern, Map, Measure, and Manage.
NIST AI Program and Center for AI Standards and Innovation — NIST’s official AI hub covering AI RMF resources, measurement science, standards, evaluations, and related federal AI programs.
OMB AI Governance Policy M-24-10 — U.S. federal agency governance and risk-management requirements for AI use.

European Union

EU AI Act — Official risk-based legal framework for AI in the European Union.
EU AI Act Summary — Secondary plain-language guide to provisions and timelines; confirm legal interpretation against the official text.

International Standards

ISO/IEC 42001:2023 — International standard for AI management systems.
ISO/IEC 23894:2023 — Guidance on risk management for AI systems.
IEEE 7000 Series — Standards for ethically aligned AI design.
OECD AI Principles — International principles for trustworthy AI.

Risk Management Frameworks

NIST AI RMF Core — Interactive starting point for NIST AI RMF categories and subcategories.
Microsoft Responsible AI Standard — Public responsible-AI standard and requirements guide.
Google PAIR Guidebook — People + AI Research guidebook for human-centered AI design.
MITRE ATLAS — Knowledge base of AI-specific adversarial tactics and techniques.
OWASP Top 10 for LLMs and GenAI Apps — Current OWASP GenAI Security Project page for LLM and generative-AI application risks and mitigations.

Governance Tools & Platforms

Microsoft Responsible AI Toolbox — Tools for error analysis, fairness, causal inference, and counterfactual analysis.
Giskard — Open-source AI quality testing platform.
Evidently AI — Evaluation, testing, and monitoring for ML and LLM systems.
WhyLabs — AI observability and model monitoring platform.
Fiddler AI — Explainability and model-performance monitoring platform.
Microsoft PyRIT — Python toolkit for generative-AI red teaming.
LangFuse — Open-source LLM observability and analytics.

AI Testing & Evaluation

Holistic Evaluation of Language Models (HELM) — Comprehensive LLM evaluation across scenarios, metrics, and models.
EleutherAI LM Evaluation Harness — Unified framework for evaluating language models.
DeepEval — LLM evaluation metrics for RAG, hallucination, and safety.
TruLens — Evaluation and tracking for LLM-based applications.
RAGAS — Evaluation framework for retrieval-augmented generation.
MLflow Model Evaluation — Model evaluation with LLM and custom-metric support.

Incident Management

AI Incident Database — Crowd-sourced database of AI incidents and failures.
AI Vulnerability Database (AVID) — Taxonomy of AI failure modes, biases, and vulnerabilities.
NIST AI RMF Core — Official NIST source for risk-management resources and supporting materials.
Weights & Biases Incident Retrospectives — Practitioner incident and operations retrospectives.

Model Cards & Documentation

Model Cards for Model Reporting — Foundational paper introducing model cards as a transparency mechanism.
Hugging Face Model Cards — Standardized model-card format and documentation.
Datasheets for Datasets — Framework for documenting dataset provenance, composition, and intended use.

Academic Papers

Concrete Problems in AI Safety — Foundational paper on practical AI safety problems.
Stochastic Parrots — Influential paper on risks of large language models.
Model Cards for Model Reporting — Introduced model cards as a documentation standard.
The Alignment Problem — Survey of specification gaming in AI systems.
Trustworthy AI — Practical guide to trustworthy machine-learning systems.
Governing AI for Humanity — UN report on global AI governance.

Datasets & Benchmarks

BIG-bench — Collaborative benchmark for large-language-model evaluation.
TruthfulQA — Benchmark for truthfulness in language-model outputs.
HarmBench — Standardized evaluation framework for automated red teaming.
MMLU — Multitask benchmark across 57 subjects.

Communities & Organizations

Partnership on AI — Multi-stakeholder organization advancing responsible-AI practice.
MLCommons — Engineering consortium for ML benchmarks and safety evaluation.
Montreal AI Ethics Institute — AI ethics research and practitioner community.
Center for AI Safety — Research organization focused on societal AI risk.
FINOS — AI-readiness resources for financial services.
NIST National AI Initiative — U.S. AI standards and research coordination.
Future of Life Institute — Research on catastrophic and long-term AI risks.

Courses & Learning

Responsible AI practices — Google’s practical responsible-AI guidance.
AI Ethics — Free course on AI and data ethics.
Trustworthy AI — IBM trustworthy-AI learning resource.
NIST AI RMF Core — Official NIST entry point for learning and implementation resources.
Human-Centered AI — Stanford HAI educational resources.

My Open-Source Frameworks

Frameworks I have built for AI governance and release readiness in regulated industries:

Repository	Description	Stars
governance-playbook	End-to-end AI governance playbook with practitioner NIST AI RMF mapping
release-checklist	Risk-tiered release gate checklist for AI/ML deployments with a CLI validator
nist-rmf-guide	Practitioner guide to implementing NIST AI RMF
release-governance	Release lifecycle framework with governance gates
accountability-patterns	Design patterns for human accountability in AI systems
regulated-ai	Starter kit for governance and release-readiness structure
multi-agent-governance	Governance framework for multi-agent AI systems
agent-eval	Framework for evaluating AI-agent performance, safety, and reliability